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Since the flight of Unmanned Aerial Vehicle (UAV) greatly consumes the limited on-board energy and affects the ability to afford computation services, it is urgent and challenging to minimize UAV’s endurance time while satisfying varieties of constraints from the UAVs and users, such as energy budgets, cache capacities, computation resources, and latency requirements. In this paper, the UAVs are endowed with the capability to temporarily buffer some offloaded tasks and process them later, then adopt cooperative computation and cache scheduling to minimize their total endurance time. Following this, the formulated problem is a mix-integer nonlinear programming problem, which can be further transformed into three more tractable subproblems: 1) PO, offloading decision and scheduling strategy for computing & caching; 2) PH, hovering trajectory design; 3) PC, computation resource allocation, and then iteratively tackled in a sequence. Due to the high complexity of solving PO, the low-complexity algorithm based on the K-means clustering and Penalty Method is proposed to reduce complexity and facilitate the processing procedure, which can save about 91.04%-189.36% running time and ONLY sacrifice 8.05%-22.08% of the total endurance time. Simulation results show that cooperative computation and cache scheduling has a great effect on saving endurance time.

Purpose This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection. Design/methodology/approach First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure. Findings The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform. Originality/value The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

Purpose In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law. Design/methodology/approach First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend. Findings This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach. Originality/value For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

The clock synchronization of mine distributed acquisition system is the key to its normal operation, and the realization of a unified time benchmark is the premise of subsequent data processing. To capture a more accurate gas migration process, in the physical similarity simulation experiment of coal and natural gas, a synchronous data acquisition system based on SQL database and distributed clock is developed to obtain synchronous real-time data. SQL database is used to establish a data collection center to solve the data exchange problem between the three units of storage, collection, and display in the system. The three units are connected in parallel, which simplifies the data flow structure in the system and improves the communication efficiency. In addition, the security and reliability of data storage are greatly improved. Based on the distributed clock, the communication structure of the data acquisition unit is constructed. In a distributed way, the data are aggregated according to the object and finally uploaded to the data acquisition center through TCP/IP (Transmission Control Protocol/Internet Protocol). It meets the requirements of synchronous communication between acquisition boards and provides a reliable data acquisition scheme for the physical simulation system of simultaneous exploitation of coal and natural gas. The physical similarity simulation experiment takes a mine in Shanxi Province as the simulation object. On the basis of this system, the physical similarity simulation experiments of gas emission under different mining conditions are carried out. In this process, the distribution maps of mining stress changes in overlying strata and gas concentration changes in different mining areas are obtained. The results of data acquisition are verified by numerical simulation. The results show that the system can effectively maintain the data acquisition of various areas in the mine.

In order to meet the requirements of digital production of assembly workshop from an enterprise, a workshop management system is presented for lean production based on the manufacturing execution system (MES). Enterprise assembly production practices and production management requirements are consulted. The requirement of building workshop management system for assembly production process is analyzed. The organization structure of assembly plant is described, and key business processes of assembly production management are modeled. The system functional structure, main use cases and data relationships are established respectively. The system implementation and the work of application investigation are completed based on the J2EE environment. Through the system development, a digital and informatization workshop management platform is set for enterprise assembly production. The intelligent measure of production management is improved, the capacity of resource allocation and production efficiency are promoted in the product assembly process.

Autonomous navigation is one of the key issues in the field of mobile robot. In recent years, as the robot system operation ability and the development of related technologies, such as image processing, people more and more research is not dependent on the autonomous navigation technology of GPS system, exist in applied to shade and interference such as GPS navigation system cannot be applied. In the mobile robot system, using the navigation sensor consists of laser ranging radar, and inertial measurement unit and RGB visual navigation system. In our research, a novel algorithm for visual signal long-distance transmission is proposed. The numerical analysis and simulation proves the correctness and robustness of the algorithm.

The main content of this paper Introduces a microcomputer based on Atmega48 wells Seal plug controller, based on method of going through steel structure to transfer control of high pressure sealing orders, designing based on the 433 MHz remote control receiver decoding hardware and software system that uses PWM technology, and composite control switches which reduce the impact of current and vibration motor to achieve a variety of control functions.