Key Node Selection Research Articles

Optimizing platoon safety through key node selection in pinning control strategy

The advent of Connected and Automated Vehicles (CAVs) and vehicular platooning has ignited high expectations for improving the safety performance of future transportation systems. Present control strategies for CAV platoons primarily concentrate on regulating each vehicle within the platoon. Although theoretically equipped to optimize a variety of performance metrics, this method is heavily dependent on communication and computational resources, which may lead to exorbitant costs. This study proposes a pinning control strategy that focuses on the selection of pivotal nodes, thereby facilitating the optimization of safety indexes for the entire platoon through the control of a select group of vehicles. The strategy encompasses two critical aspects: the identification of key control nodes and the application of node-based control. Initially, a feature matrix that includes both the intrinsic dynamics of the vehicles and the dynamics relative to other vehicles is developed. Spectral clustering is then utilized to identify these pivotal nodes. Subsequently, specialized control mechanisms for these nodes are devised, based on Proportional-Integral-Derivative (PID) controllers, with the objective of improving platoon safety. The efficacy of this strategy is corroborated through numerical simulations. To assess the safety performance, four categories of Surrogate Safety Measures (SSMs), which consider distance and acceleration, are applied. The findings confirm that the pinning control strategy significantly enhances performance across various SSMs, considerably reducing the risk of collisions during platoon operations. Moreover, it improves operational efficiency and platoon stability, establishing its value as an effective method for safety enhancement.

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

Robot Path Planning Research Incorporating Improved A* Algorithm and DWA Algorithm

For the traditional A* algorithm has problems such as long paths, large number of nodes, and the demand for dynamic obstacle cannot be avoided in complex environment. A mobile robot dynamic path avoidance method will be improved to improve the A * algorithm and improve DWA algorithm Two map environments are used for simulation verification. First, the evaluation function and key node selection strategy are optimized for the A* algorithm, and redundant nodes are deleted; then the dynamic obstacle distance evaluation function is added to the DWA algorithm which for the purpose of the obstacle avoidance performance can be enhanced. The results about the improved A* algorithm reduces 12.20% and 58.33% in path length and number of turning points respectively compared with the traditional A* algorithm can be obviously grasped by the simulation experiment; by using the fusion algorithm whose purpose of using arcs instead of the straight lines is to turn more smoothly, and can be closest to the global optimum while avoiding dynamic obstacles to complete the search.

Key Nodes Selection in Controlling Complex Networks via Convex Optimization.

Key nodes are the nodes connected with a given number of external source controllers that result in minimal control cost. Finding such a subset of nodes is a challenging task since it impossible to list and evaluate all possible solutions unless the network is small. In this paper, we approximately solve this problem by proposing three algorithms step by step. By relaxing the Boolean constraints in the original optimization model, a convex problem is obtained. Then inexact alternating direction method of multipliers (IADMMs) is proposed and convergence property is theoretically established. Based on the degree distribution, an extension method named degree-based IADMM (D-IADMM) is proposed such that key nodes are pinpointed. In addition, with the technique of local optimization employed on the results of D-IADMM, we also develop LD-IADMM and the performance is greatly improved. The effectiveness of the proposed algorithms is validated on different networks ranging from Erdős-Rényi networks and scale-free networks to some real-life networks.

An Effective Kernel Online Learning Algorithm with Sparse Update for Electronic Devices Condition Prediction

In order to implement condition prediction for electronic devices, a new online learning algorithm for extreme learning machine with kernel (KELM) is proposed. For key nodes selection, a sparse dictionary with predefined size is constructed adaptively by online minimization of its cumulative coherence. Meanwhile, for model coefficients update, an improved decremental learning algorithm is presented by using elementary transformation of Gram matrix and block matrix inversion formula. The performance of proposed algorithm is compared with several well-known online sequential ELM algorithms. The simulation results show that the proposed algorithm has higher prediction accuracy and better stability. Thus it is suited to online condition monitoring of electronic devices.

Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

Smart media and application

Smart media and application

Key node selection based on a genetic algorithm for fast patching in social networks

SummaryOnline social network users provide considerable amounts of personal information and share this information with friends without space‐time limitations. The tight connectivity among users of social networks causes the rapid spreading of information. Given the popularity of social networking sites, there is a high probability of attacks. Worms target popular users with interesting information to infect them, as their higher reputations have more power in social networks. Therefore, timely patch propagation schemes must be able to inhibit the activity of worms. To improve the patch propagation speed, it is important to select key nodes that are the starting points of the patch process. In this paper, we proposed a key node selection scheme based on a genetic algorithm to find the most significant contribution nodes of patch propagation. We modeled the usage patterns of an online social network user and simulated the proposed scheme with data from this user. Simulation results show that the proposed scheme propagates patches more rapidly than existing schemes.

Distributed Conflict-Detection and Resolution Algorithms for Multiple UAVs Based on Key-Node Selection and Strategy Coordination

A distributed conflict-detection (CD) and resolution algorithm for multiple unmanned aerial vehicles based on key-node selection and strategy coordination are proposed in this paper. Each aircraft is regarded as a critical node in a complex network. A novel temporal and spatial integrated CD methodology is proposed for aircraft formations and clusters. Each node selects three candidate strategies from a pre-set pool of strategies and generates corresponding planned tracks using uncertainty modeling based on the 6 degrees-of-freedom motion. The primary combination of strategies required for coordination of the aircraft in conflict is based on the maximum-robustness principle. To address partial knowledge of the environment, a special token-allocation strategy for coordination with incomplete information is proposed in this paper. To address potential data dropouts, this study damps the solution to achieve coordination. Two extreme scenarios are constructed to examine the proposed methodology. The results showed great collision-avoidance ability in continuous conflicts with multiple aircraft in a highly dynamic 3-D environment.

L0 norm constraint based external control source allocation for the minimum cost control of directed networks

Locating a pre-given number of key nodes that are connected to external control sources so as to minimize the cost of controlling a directed network ẋ(t)=Ax(t)+Bu(t), known as the minimum cost control problem, is of critical importance. Considering a network consisting of N nodes with M external control sources, the state of art techniques employ iterative searching to determine the input matrix B that characterizes how nodes are connected to external control sources, in a matrix space RN×M. The nodes having M largest values of a defined importance index are selected as key nodes. However, such techniques may suffer from large performance penalty in some networks due to the diversity of real-life networks. To address this outstanding issue, we propose an iterative method, termed “L0-norm constraint based projected gradient method” (LPGM). We probabilistically search the input matrix in each iteration by restricting its L0 norm as a fixed value M, which implies that each control source is always only connected to a single key node during the whole searching process. Simulation results show that the solution always efficiently approaches a suboptimal key node set in a few iterations. These results provide a new point of view regarding the key nodes selection in the minimum cost control of directed networks.

Key‐nodes selection problem for minimum cost control of directed networks

SummaryThe key‐nodes selection problem is to determine key nodes from all those connected to external control sources so that the minimum cost is achieved when they are used to control a directed network. Clearly, it is an important issue in both theory and application. But its solution still remains open because of the difficulty in analyzing the graphical properties of key nodes. We present a method, called normalized projected gradient method (NPGM), to address this critical issue. An index notation arrangement‐based chain rule is proposed to obtain the gradient of a defined cost function where matrix‐by‐matrix derivatives are involved. In addition, projection and normalization operators are used to establish the convergence property of NPGM. Simulation results also demonstrate its satisfactory performance. We believe that the presented results of NPGM not only provide a technical breakthrough, but also our discussions on them reveal certain important physical insights regarding key nodes selection in controlling complex directed networks.

Key node selection in minimum-cost control of complex networks

Finding the key node set that is connected with a given number of external control sources for driving complex networks from initial state to any predefined state with minimum cost, known as minimum-cost control problem, is critically important but remains largely open. By defining an importance index for each node, we propose revisited projected gradient method extension (R-PGME) in Monte-Carlo scenario to determine key node set. It is found that the importance index of a node is strongly correlated to occurrence rate of that node to be selected as a key node in Monte-Carlo realizations for three elementary topologies, Erdős–Rényi and scale-free networks. We also discover the distribution patterns of key nodes when the control cost reaches its minimum. Specifically, the importance indices of all nodes in an elementary stem show a quasi-periodic distribution with high peak values in the beginning and end of a quasi-period while they approach to a uniform distribution in an elementary cycle. We further point out that an elementary dilation can be regarded as two elementary stems whose lengths are the closest, and the importance indices in each stem present similar distribution as in an elementary stem. Our results provide a better understanding and deep insight of locating the key nodes in different topologies with minimum control cost.

An energy-efficient query based on variable region for large-scale smart grid

The state-of-the-art query techniques in power grid monitoring systems focus on querying history data, which typically introduces an unwanted lag when the systems try to discover emergency situations. The monitoring data of large-scale smart grids are massive, dynamic and highly dimensional, so global query, the method widely adopted in continuous queries in Wireless Sensor Networks (WSN), is rendered not suitable for its high energy consumption. The situation is even worse with increasing application complexity. We propose an energy-efficient query technique for large-scale smart grids based on variable regions. This method can query an arbitrary region based on variable physical windows, and optimizes data retrieve paths by a key nodes selection strategy. According to the characteristics of sensing data, we introduce an efficient filter into the each query subtree to keep non-skyline data from being retrieved. Experimental results show that our method can efficiently return the overview situation of any query region. Compared to TAG and ESA, the average query efficiency of our approach is improved by 79% and 46%, respectively; the total energy consumption of regional query is decreased by 82% and 50%, respectively.

Study on Gravity Dam Foundation Stability Combining with Dam Foundation Deformation Behavior

Gravity dam foundation failure progress is always associated with its deformation development. So dam foundation deformation should be paid more attention in gravity dam stability analysis. Key node plays an important role in analyzing the dam foundation deformation development. But how to select key node is worth researching. A gravity dam foundation stability is studied by the development of plastic zone and key nodes’ displacements in dam foundation. The problem of key node selection is researched and a key node selection method is brought up also in this paper.

2D 이미지를 활용한 인체치수 구현 프로세스 개발

Body sizing of has been recognized as an important element affecting the degree of customer satisfaction in the apparel industry. Recent developments in IT technologies have enabled more studies in custom-made apparel systems that comply with the diverse demands from customers in many countries. Diverse methods to obtain personal physical size are being studied. This study estimates the accuracy by developing the system in which the data of length and girth can be calculated through changing a modeling by comparing the data with circular 3-dimensional physical configuration data. This information was computed from the process (such as the conversion to a standardize image) which utilizes the image capture of 2-dimensional three sides (front, side, and rear), contour tracing, and key-node selection and by realizing it in the real world.