Design and Control of Advanced Mechatronics Systems

Research and experiments on mechatronics systems, which are a synergistic integration of mechanical engineering, electronic control, and systems concepts, have contributed significantly to the design of systems, devices, processes, and products [...

Design and Control of Advanced Mechatronics Systems

Research and experiments on mechatronics systems, which are a synergistic integration of mechanical engineering, electronic control, and systems concepts, have contributed significantly to the design of systems, devices, processes, and products [...

Non-coal mine safety supervision mode based on risk monitoring and early warning

At present, non-coal mine safety supervision mainly includes safety management inspection and on-site safety supervision inspection. They rely on safety information management platform and on-site inspection by safety inspectors respectively. There are problems of low supervision efficiency, delayed information acquisition, and it is difficult to realize dynamic and comprehensive supervision. In view of the above problems, this paper puts forward a non-coal mine safety supervision mode based on risk monitoring and early warning, considering the characteristics of non-coal mines, such as large number, scattered, poor, small, and the differences in risks and informatization management and control of different types of non-coal mines. Based on the risk monitoring and early warning system of non-coal mine, the risk monitoring index data in five aspects of personnel, environment, equipment and facilities, management and monitoring topics are extracted. The single risk early warning analysis and comprehensive risk alarm analysis are carried out in two ways of risk point and risk surface respectively. The graded push of single risk early warning and the graded control of comprehensive risk alarm can be realized. The monitoring data and special topics of various types of non-coal mines are sorted out. The trigger and disposal mechanism of single risk graded early warning is introduced. This study focuses on the construction process of the model for comprehensive risk research and judgment and graded supervision. Based on the risk monitoring data of non-coal mines, the risk monitoring index system is established. The entropy weight method is adopted to give weight to the risk monitoring index, and the scoring standard is formulated to score the risk index of non-coal mines. Based on the index weight and score, the comprehensive risk level of mining enterprises is determined. The non-coal mine safety supervision mode based on risk monitoring and early warning realizes the graded supervision and inspection of non-coal mines by supervision departments under different supervision periods and ranges. The mode optimizes the allocation of supervision and inspection resources, and improves the efficiency of supervision and inspection

DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment. Note to Practitioners —The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability

Isolation and Aflatoxin B1-Degradation Characteristics of a Microbacterium proteolyticum B204 Strain from Bovine Faeces

Aflatoxin B1 (AFB1) is one of the most harmful mycotoxins, raising serious global health and economic problems. Searching for biological approaches for effective and safe AFB1 degradation is imminent. In our study, Microbacterium proteolyticum B204 isolated from bovine faeces degraded 77% of AFB1 after 24 h, becoming the first reported bacteria from the Microbacterium family to possess AFB1 degradation characteristics. Temperature variation showed little effect on its degradation ratio, demonstrating high thermostability of 75% and 79% after boiling and sterilization, respectively. We suppose that the components playing a key role during this process were proteins, considering the decreased degradation rate caused by Proteinase K. Cell viability detection on HepG2 cells indicated that the degradation products were much less toxic than pure AFB1. Furthermore, B204 cell-free culture supernatant also degraded AFB1-contaminated food, such as peanuts, corn and cheese. These results suggested that this strain with AFB1 degradation properties could be a prospective candidate for application in the food and feed industries