Highly automated assembly lines enable significant productivity gains in the manufacturing industry, particularly in mass production condition. Nonetheless, challenges persist in job scheduling for make-to-job and mass customization, necessitating further investigation to improve efficiency, reduce tardiness, promote safety and reliability. In this contribution, an advantage actor-critic based reinforcement learning method is proposed to address scheduling problems of distributed flexible assembly lines in a real-time manner. To enhance the performance, a more condensed environment representation approach is proposed, which is designed to work with the masks made by priority dispatching rules to generate fixed and advantageous action space. Moreover, a Monte-Carlo tree search based soft shielding component is developed to help address long-sequence dependent unsafe behaviors and monitor the risk of overdue scheduling. Finally, the proposed algorithm and its soft shielding component are validated in performance evaluation.
Sea clutter is the backscattered echo of radar irradiating the sea surface, and this signal data includes not only pure sea clutter, but also often accompanied by many interference signals and noise. Based on the effective sea clutter region in the radar echo image, certain characteristics of the sea clutter can be accurately calculated, which plays a crucial role in the subsequent study of sea clutter and radar performance. The traditional method of using manual detection of effective sea clutter region often requires a lot of time, and with the rapid development of artificial intelligence technology in recent years, it is necessary to establish an effective sea clutter region detection method based on deep learning in Range-Pulse (RP) images. In this paper, based on the sea clutter data collected by shore-based ultra high frequency (UHF) radar, an improved model (H-YOLOv4) based on YOLOv4 network architecture is proposed to solve this problem, which uses the H-swish activation function to improve the performance of the model for effective sea clutter region extraction. In our test set of sea clutter RP images, it is experimentally demonstrated that the AP50 of the model reaches 89.75% with a score threshold of 0.25, which over state-of-the-art method. Compared with the traditional manual detection methods, the model can quickly and accurately extract the effective sea clutter region and maintain a good detection speed.
The microprocessor suitable for the physical network is studied, and a processor model more suitable for the Internet of Things is proposed. This solution adopts the structure of five-stage pipeline and tailors the MIPS instruction set architecture CPU. The designed data path is more concise, the power consumption is lower, and it has a high degree of scalability, making it more suitable for the Internet of Things field. The scheme was tested on the EDA platform. The test took the temperature and humidity of the greenhouse as the test object. The test results showed that the scheme met the design requirements.
With the rapid development of social economy and the acceleration of urbanization, energy consumption has increased rapidly, and power resources have become a scarce resource. In order to actively respond to the national green lighting strategy, this paper uses wireless sensor network and Internet of Things technology to design a smart street light management system, and incorporate urban street lights into the new urban Internet of Things system. The system collects the data of the surrounding environment of the street lamp in real time through the street lamp controller with multiple types of sensors installed in the street lamp pole, receives the data of the street lamp node through the LoRa network, and then uploads the data to the cloud monitoring platform through the NB-IoT technology. The city street lamp information is effectively collected and stored; at the same time, combined with WEBGIS technology, the current, voltage, power and other street lamp status data are visually displayed in the electronic map. For abnormal data, the system analyzes, locates and alarms, so as to realize the information interaction between the street light and the cloud platform, the precise control of the street light, and the intelligent monitoring and management. The experimental results show that the system has good stability, can effectively save energy and reduce consumption, and save the costs of management and operation and maintenance.
The external power receiving capability of the overall AC/DC receiving-end system is mainly restricted by the HVDC bipolar blocking failure. This paper presents a method to evaluate the maximum AC external receiving capability of the receiving-end power grid. Firstly, the power flow transfer ratio of the AC transmission line is analyzed after the HVDC bipolar blocking occurs in the receiving-end power grid under the given operation mode. Then, according to the thermal stability limit constraint of the AC transmission line, the initial power flow is calculated when the power flow just satisfies the power constraint after the power flow transfer. The calculated initial power flow is used as the optimal power flow distribution under the maximum power receiving capability of the receiving-end power grid. Next, the power flow transfer capability indexes of AC transmission lines and transformers and the maximum power receiving capability margin are defined. The objective function is established to analyze and evaluate the maximum external receiving capability of the receiving-end power grid under different operation modes. Finally, the effectiveness and adaptability of the proposed method are verified by taking a provincial receiving-end power grid as an example.
