Research on Automation Control of University Logistics Management System Based on Wireless Communication Network

Nowadays, individuals with and without technical knowledge have started utilizing the technology to a broader extent. The utilization of technology has gone deeper concerning gadgets that aid in wireless communication with anyone or anything. This advancement has paved the way for a trending technology named wireless communication network (WCN) in recent years. In addition to this, there has been a significant change and development in the field of trading goods. Manual ordering of goods has changed to online ordering, and hence, supply chain management. This research focuses on applying WCN to a logistic tracking information system (LTIS) for a university with automatic control of the system. A novel algorithm named intelligent logistics system construction algorithm is implemented to evaluate the efficiency and performance of data. This model aids in the tracking goods and automatic control of university logistics.

The Use of ZigBee Wireless Communication Technology in Industrial Automation Control

With the accelerating process of world industrialization, countries pay more attention to industrial modernization. In order to solve the problems of traditional control technology and system with low accuracy, high energy consumption, and high delay in industrial automation control and the difficulty in adapting to the new industrial production process, the new industrial automation control system is discussed and studied. The industrial automation control is studied, and a new monitoring system is designed for automation control by using ZigBee wireless communication technology. The design of the system data communication scheme is completed by the in-depth study and summary of ZigBee wireless communication technology. The hardware structure and software structure of ZigBee wireless communication module are designed, and the wireless monitoring system using ZigBee is established. Taking the production workshop of a pharmaceutical factory as the monitoring object, the working stability and data transmission efficiency of the model are tested. The experiment shows that the ZigBee wireless transmission model has lower temperature and better stability than the wired transmission system in the working process. Compared with the traditional wired control system, the temperature of ZigBee wireless control system is lower when it works. The difference between the highest temperature and the lowest temperature is 0.1°C. In the overall work flow, the system temperature is lower than that of the wired control. ZigBee wireless control has good stability. Subsequently, the method of manually setting obstacles is used to test the effectiveness of data transmission. The experiment found that in the absence of barrier, the maximum effective transmission distance was 40 meters, and the packet loss rate was very low, which could ensure the timeliness of data. Therefore, this system can be applied to industrial automation control. The research results can provide some reference for the design of new control systems in other industrial control fields. The improved system can also be applied to other industries. ZigBee is a short-range technology for wireless sensor networks, and it is a two-way communication technology with low power consumption, low data rate, and low cost. It can be applied to automatic control and remote control. This provides a platform for wireless networking and control of small and cheap devices. ZigBee will not compete with connection technologies such as Wi-Fi. Meanwhile, it will greatly stimulate the progress of the industrial market.

Simulation-Based Evaluation of Variation in Left-Turn Paths in the Coordinated Intersection Management

Coordinated intersection management (CIM) has gained more attention with the advance of connected and autonomous vehicle technology. The optimization of passing schedules and conflict separation between conflicting vehicles are usually conducted based on the predefined travelling paths through the intersection area in the CIM. In real-world implementation, however, the diversity of turn paths exists due to multiple factors such as various vehicle sizes and automation control algorithms. The aim of this paper is to investigate how the variation in left-turn paths affects the feasibility and viability of optimal passing schedules, as well as the safety and efficiency of intersection operation. To do this, we start with identifying six typical left-turn paths to represent the variation. A scenario-based simulation is first conducted by using each of the paths as the nominal path. The optimal schedules and the corresponding alternative schedules are generated to calculate indicators for nominal performance, average performance, and robustness. The best path is selected in terms of schedule optimality and robustness. With schedules obtained by solving CIM models using the selected path, the left-turning CAVs are assumed to travel along one of the six paths randomly to simulate the path divergence. A surrogate safety measure, PET, is utilized to assess the safety of the intersection under CIM. The theoretical PET with the nominal path and the actual PET with the random path are calculated for each conflict event. Comparisons of two PET sets show the increase in conflict risk and vehicle delay. The conclusion can be drawn that the variation in left-turn paths causes the decline in safety level and travelling efficiency and should be considered in the CIM model to ensure safe and efficient implementation in the intersection.

SIMULASI NUMERIK KENDALI KAPAL CEPAT TAK BERAWAK DUA PROPELLER TANPA RUDDER

This paper discusses the simulation of automation control and hull manufacturing on a fast boat. This high-speed boat that will be carried out for fish mapping. This study uses a numerical simulation to predict its motion. The experiment was carried out 3 times in forward motion, and 3 times in turn motion. The ship’s time and movement in ideal conditions will be recorded for each experiment. With the PID value for forward overshoot with values such as: 8.94%, 30.86%, and 40.60%, while turning overshoot is obtained, among others: 25.77%, 49.82%, and 61.15% Then the advanced settling time obtained includes: 0.91 seconds, 3.84 seconds, and 5.31 seconds. The settling time for turns is: 1,022 seconds, 0.85 seconds, and 0.84 seconds These values are obtained from the simulation graph of the three-dof formulas for surge, sway, yaw.