The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model b...
详细信息
The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model based on distributed compressive sensing and a bidirectional long-short memory network to classify power quality disturbances. A cloud-edge collaborative framework is first established with distributed compressed sensing as an edge-computing algorithm. With the uploading of dictionary atoms of compressed sensing, the data transmission and feature extraction of power quality is achieved to compress power quality measurements. In terms of data transmission and feature extraction, the dictionary atoms and measurements uploaded at the edge are analyzed in the cloud by building a cloud-edge collaborative framework with distributed compressed sensing as the edge algorithm so as to achieve compressed storage of power quality data. For power disturbance identification, a new network structure is designed to improve the classification accuracy and reduce the training time, and the training parameters are optimized using the Deep Deterministic Policy Gradient algorithm in reinforcement learning to analyze the noise immunity of the model under different scenarios. Finally, the simulation analysis of 10 common power quality disturbance signals and 13 complex composite disturbance signals with random noise shows that the proposed method solves the problem of inadequate feature selection by traditional classification algorithms, improves the robustness of the model, and reduces the training time to a certain extent.
Rigid joint manipulators are limited in their movement and degrees of freedom (DOF), while continuum robots possess a continuous backbone that allows for free movement and multiple DOF. Continuum robots move by bendin...
详细信息
Rigid joint manipulators are limited in their movement and degrees of freedom (DOF), while continuum robots possess a continuous backbone that allows for free movement and multiple DOF. Continuum robots move by bending over a section, taking inspiration from biological manipulators such as tentacles and trunks. This paper presents an implementation of a forward kinematics and velocity kinematics model to describe the planar continuum robot, along with the application of reinforcement learning (RL) as a control algorithm. In this paper, we have adopted the planar constant curvature representation for the forward kinematic modeling. This choice was made due to its straightforward implementation and its potential to fill the literature gap in the field RL-based control for planar continuum robots. The intended control mechanism is achieved through the use of Deep Deterministic Policy Gradient (ddpg), a RL algorithm that is suited for learning controls in continuous action spaces. After simulating the algorithm, it was observed that the planar continuum robot can autonomously move from any initial point to any desired goal point within the task space of the robot. By analyzing the results, we wanted to recommend a future direction for research in the field of continuum robot control, specifically in the application of RL algorithms. One potential area of focus could be the integration of sensory feedback, such as vision or force sensing, to improve the robot's ability to navigate complex environments. Additionally, exploring the use of different RL algorithms, such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO), could lead to further advancements in the field. Overall, this paper demonstrates the potential for RL-based control of continuum robots and highlights the importance of continued research in this area.
With the rapid development of modern financial technology, the use of machine learning methods to assist enterprises in financial decision-making has become an important trend. This article investigates financial deci...
详细信息
With the rapid development of modern financial technology, the use of machine learning methods to assist enterprises in financial decision-making has become an important trend. This article investigates financial decision-making problems based on the Deep Deterministic Policy Gradient (ddpg). By building a simulation environment and organically integrating the algorithm with the real financial decision-making process, the effectiveness and accuracy of the ddpg algorithm in financial decision-making were verified. The value of the investment portfolio steadily increases with the increase of time step. From the initial $10000 to the final $10985, this indicates that the algorithm is highly effective in optimizing asset portfolios. This development trend demonstrates that the ddpg algorithm can efficiently manage assets in a stable market, thereby achieving the goal of preserving and increasing value. The research results of this article can expand the application of ddpg method in unconventional data processing, and provide new ideas and methods for the research of financial technology and other related issues, which has important theoretical significance and practical value.
With the rapid development of Software Defined Networking (SDN) technology, how to efficiently and flexibly manage and allocate network resources has become a key challenge. This article proposes the ddpg (Deep Determ...
详细信息
With the rapid development of Software Defined Networking (SDN) technology, how to efficiently and flexibly manage and allocate network resources has become a key challenge. This article proposes the ddpg (Deep Deterministic Policy Gradient) algorithm method, aiming to dynamically optimize resource allocation in SDN. The ddpg algorithm can respond in real-time to changes in network status, automatically adjust resource allocation strategies, and thereby improve network performance and service quality. This study comprehensively evaluated the dynamic resource allocation ability of neural network-based ddpg reinforcement learning algorithm in SDN through four experiments. In the baseline comparison experiment, the network throughput of ddpg reached 95 Mbps. Under different network loads, ddpg still maintains a throughput of 95 Mbps under high load conditions. In the fault recovery capability testing experiment, the recovery time of ddpg is 30 seconds. In the final real-time adjustment capability evaluation, ddpg demonstrated a fast response time of 1.2 seconds, as well as a throughput of up to 80 Mbps and a resource utilization rate of 95% after adjustment. From the experimental data conclusions, it can be seen that the ddpg algorithm provides superior performance and flexible resource management capabilities in SDN environments.
The need for a safe and reliable transportation system has made the advancement of autonomous vehicles (Avs) increasingly significant. To achieve Level 5 autonomy, as defined by the Society of Automotive Engineers, AV...
详细信息
The need for a safe and reliable transportation system has made the advancement of autonomous vehicles (Avs) increasingly significant. To achieve Level 5 autonomy, as defined by the Society of Automotive Engineers, AVs must be capable of navigating complex and unconventional traffic environments. Path-following is a crucial task in autonomous driving, requiring precise and safe navigation along a defined path. Traditional path-tracking methods often rely on parameter tuning or rule-based approaches, which may not be suitable for dynamic and complex environments. Reinforcement learning has emerged as a powerful technique for developing effective control strategies through agent-environment interactions. This study investigates the efficiency of an optimized Deep Deterministic Policy Gradient (ddpg) method for controlling acceleration and steering in the path-following of autonomous vehicles. The algorithm demonstrates rapid convergence, enabling stable and efficient path tracking. Additionally, the trained agent achieves smooth control without extreme actions. The performance of the optimized ddpg is compared with the standard ddpg algorithm, with results confirming the improved efficiency of the optimized approach. This advancement could significantly contribute to the development of autonomous driving technology.
暂无评论