Advanced Control for Applications最新文献

Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

This paper presents a comprehensive stability analysis of the boundary-based hybrid control (BBHC) algorithm designed for boost converter. The stability assessment is carried out utilizing multiple Lyapunov functions, addressing both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operation. The boost converter is modeled as a hybrid automaton to capture its dynamic behavior accurately. Through rigorous Lyapunov stability analysis, this study demonstrates the effectiveness of the BBHC algorithm in ensuring stable operation of the boost converter across various operating modes. Additionally, the proposed control algorithm's validation is conducted using the FPGA-in-the-loop (FIL) technique, highlighting its efficiency and robustness in real-world applications. This research contributes valuable insights into the design and implementation of stable control strategies for boost converter, emphasizing the practical utility of the BBHC algorithm with FIL for enhanced performance and reliability in power electronics systems.

At present, there is no standard practice for temporary connection of component installation. How to make temporary connections to components more efficient and accurate is the key to the construction of cast-in-place connection parts in concrete prefabricated buildings. In order to improve the assembly technology of concrete structures, this paper combines three-dimensional intelligent image analysis technology for simulation, analyzes and compares several common electronic image stabilization algorithms, discusses the video image stabilization technology based on gray projection method in detail, and gives experimental results. Moreover, this paper analyzes and compares common moving target tracking algorithms, such as feature-based tracking, region matching-based tracking, dynamic contour-based tracking, and 3D model-based tracking. In addition, this paper studies the target tracking algorithm based on the Camshift algorithm, and constructs the assembly model of the intelligent concrete structure with the support of the algorithm. Through experimental verification, it is known that the performance distribution of the concrete structure assembly model based on 3D intelligent image analysis in experimental evaluation is between [81, 89], the experimental study shows that the concrete structure assembly model based on 3D intelligent image analysis can effectively improve the assembly effect of concrete structure.

Digital technology still has a low level of intelligence in the microgrid mode of teaching behavior analysis, resulting in the traditional manual observation and recording stage still being used for speaker identity classification, and the efficiency of teaching behavior analysis is also low. In response to the above issues, the research is based on the teacher-student analysis method and proposes a dual clustering algorithm based on the general background model Gaussian mixture model for speaker identity classification, thereby realizing the development and design of intelligent behavior analysis software. The research results indicate that the average recall rate of behavior transition points in the classroom teaching discourse corpus of the intelligent behavior analysis software is 89.03%, which is better than traditional analysis methods. Therefore, the intelligent behavior analysis software constructed by the dual clustering algorithm has high effectiveness and practicality. The research proposes a method model and implements intelligent visualization for classroom teaching behavior analysis, improving the efficiency of analyzing current microgrid teaching behavior.

With the widespread popularity of smart wearable devices and the rise of emerging Internet of Things applications, such as smart cities, smart homes, and smart cars, the demand for Internet of Things devices is growing. The technology for positioning Internet of Things nodes using traditional wireless sensors only provides approximate location information, which is insufficient for high-precision applications. To achieve accurate sensor node location in a specific area, this study proposes an advanced weighted distance vector jump location algorithm. This paper proposes using optical wireless networks, a new wireless communication technology, to enhance the distance vector jump algorithm. It is considered the core technology in researching the three-dimensional positioning of wireless sensor IoT nodes. The experimental data validated that by comparing with existing positioning algorithms, the improved algorithm significantly improved the location accuracy, and its average orientation error was significantly lower than other algorithms. In three cases where the wireless sensor communication radius was between 10 and 30 m, the average positioning errors of the improved algorithm were 0.363, 0.264, and 0.258, respectively. Compared with the pre improved Distance Vector Hop algorithm, its accuracy has increased by 41.1%, indicating the better positioning performance. Overall, the improved weighted algorithm significantly improves the positioning effect, providing strong technical support for the three-dimensional positioning of wireless sensor Internet of Things nodes.

In order to improve the trajectory control effect of multi-degree-of-freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi-degree-of-freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi-degree-of-freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non-replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto-Encoder (DSAE) model is constructed and trained using a layer-wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto-Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back-Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

In order to improve the precision of earth pressure balance control and anti-interference ability of shield sealing chamber, this paper proposes a nonlinear control strategy for the screw conveyor based on disturbance observer and back-stepping method, so as to ensure the safe and efficient tunneling of the shield machine. According to the hydraulic flow dynamic balance principle of shield machine, the mechanism model of electro-hydraulic screw conveyor system is established, and the system state space model is derived. The nonlinear controller of the screw conveyor is designed by using the inverse step method and the disturbance observer compensation characteristic, so that the system responds quickly and compensates for the flow disturbance and external force disturbance in real time. At last, the system stability is proven by using the Lyapunov function. The experimental results show that the method has high control accuracy with fast response and strong anti-interference ability.

With regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real-time performance and accuracy in terms of occlusion handling and tracking loss.

This research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real-time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real-time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.