Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.

Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

UAV and Its Approach in Oil and Gas Pipeline Leakage Detection

The synergy of vibration and gas sensors with unmanned aerial vehicles for a low-response-time Leakage Detection System (LDS) is explored in this work. Several pipeline accidents have occurred, most of which were triggered by untimely detection of pipe leakages in systems conveying oil and gas in many developing countries. The consequences of this include human casualties, environmental degradation, economic loss, and loss of resources. To limit the damages caused by inevitable leakages, a low-time-response system for leakage detection is required. Response time derived from the LDS is compared to the typical response time obtained from an existing system to determine the efficiency of the developed system. A comparative analysis of the response time of the designed LDS and existing systems reveals that the designed LDS response time is 1146.7% faster and having a pictorial view of the localized area of interest would go a long way to preventing unnecessary mobilization for site visits and eradicating the costly effect of false alarms.

Optimization of Computer Communication Monitoring System for Wind Turbine Speed

This article first studies the operating principles of wind turbines, focusing on the analysis of the structure and working principles of permanent magnet direct-drive wind turbines. According to the actual needs of the wind power system, the monitoring objects of the monitoring system are determined, and the overall monitoring plan for wind power generation is proposed to realize real-time analysis of the operating characteristics of the wind power system. At the same time, it pointed out the great significance of the wind power generation simulation experiment system and focused on the wind speed modeling. In terms of hardware research and analysis, relevant sensors, high-speed data acquisition cards, etc., were selected, and relevant signal conditioning circuits were designed, and a permanent magnet direct-drive wind power generation system simulation monitoring platform was constructed. In terms of software, LabVIEW was chosen as the design language of the monitoring system, and it pointed out the advantages of using LabVIEW in this monitoring system. Finally, the system uses the laboratory permanent magnet direct-drive wind turbine as the monitoring object. The practicality and accuracy of the system are verified through experiments such as permanent magnet motor power test, motor speed test, database system test, and remote monitoring test. The experimental results show that the monitoring system has a friendly interface and perfect functions and has important practicability and reference in the field of wind power monitoring.

A Novel Self-Positioning Based on Feature Map Creation and Laser Location Method for RBPF-SLAM

Aiming at the high computational complexity of the traditional Rao-Blackwellized Particle Filtering (RBPF) method for simultaneous localization and Mapping (SLAM), an optimization method of RBPF-SLAM system is proposed, which is based on lidar and least square line segment feature extraction as well as raster, reliability mapping continuity. Validation test results show that less storage in constructing a map with this method is occupied, and the computational complexity is significantly reduced. The effect of noise data on feature data extraction results is effectively avoided. It also solves the problem of error accumulation caused by noninteger grid size movement of unmanned vehicle in time update stage based on Markov positioning scheme. The improved RBPF-SLAM method can enable the unmanned vehicle to construct raster map in real time, and the efficiency and accuracy of map construction are significantly improved.

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.

Multistage Feature Complimentary Network for Single-Image Deraining

Rain will cause the occlusion and blur of background and target objects and affect the image visual effect and subsequent image analysis. Aiming at the problem of insufficient rain removal in the current rain removal algorithm, in order to improve the accuracy of computer vision algorithm in the process of rain removal, this paper proposes a multistage framework based on progressive restoration combined with recurrent neural network and feature complementarity technology to remove rain streak from single images. Firstly, the encoder-decoder subnetwork is adapted to learn multiscale information and extract richer rain features. Secondly, the original resolution image restored by decoder is used to preserve refined image details. Finally, we use the effective information of the previous stage to guide the rain removal of the next stage by the recurrent neural network. The final experimental results show that a multistage feature complementarity network performs well on both synthetic rainy data sets and real-world rainy data sets can remove rain more completely, preserve more background details, and achieve better visual effects compared with some popular single-image deraining methods.

Use of the Human Walking Gait Cycle for Assistive Torque Generation for the Hip Joint Exoskeleton

The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.

Design and Performance Simulation of Computer Control System for Automatic Monitoring of Upper Computer Communication Operation State

The upper computer communication operation state automatic monitoring system is mainly used to remotely monitor the equipment, obtain various parameter indexes in the operation process of remote equipment, realize remote monitoring and fault diagnosis, and improve the management efficiency of decentralized equipment. This paper completes the design of communication, data storage, query, and other subsystems of upper and lower computers. The lower computer establishes a data channel with the OPC server through the MPI protocol and uploads the collected data to the OPC server in real time. The upper computer reads the data through the OPC server and displays the changes of monitored parameters in real time through the monitoring interface, so as to give an alarm under abnormal conditions. In addition, since the default database of Kingview is access, considering that the Microsoft Access database can store up to 2G of content, in order to upgrade and expand the subsequent system, SQL Server database is selected for data query, backup, and saving. The parameter setting method of communication control system is analyzed, the simulation model of industrial boiler control system is established by using Matlab/Simulink, and the interface between host computer software (IBCCS-e) and the model is provided. This paper analyzes the results of communication parameter adjustment. The simulation results show that the industrial boiler computer control system (IBCCS) has stable performance, low cost, convenient operation, and good maintainability. After further improvement, it has certain application value in the operation transformation of new small- and medium-sized boilers and original boilers.

Parking Space Detection and Path Planning Based on VIDAR

The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.