Applications of wireless sensor networks to improve occupational safety and health in underground mines

Data mining of accidents in Spanish underground mines in the period 2003-2021 caused by a collision with a moving object

Underground mining is currently one of the Spanish economic sectors with the worst accident rates. Besides, the most frequent type of accident, and with the most serious consequences, is the one in which the injured worker is hit by a moving object. For this reason, this study focuses on the analysis of this type of accident, divided into 3 subgroups to better understand the behavioural patterns. Data mining techniques were applied using the Apriori algorithm to extract as much information as possible about the genesis of these accidents. Similarly, each subset of accidents was processed in two different ways to improve the data analysis, depending on the causal variables used in each case, so that a study of six different scenarios was carried out. The five best association rules or behaviour patterns for each of the six scenarios are shown as a function of their frequency for each rule with 1-4 causal variables.

Gas nanosensors for health and safety applications in mining

The ever-increasing demand for accurate, miniaturized, and cost-effective gas sensing systems has eclipsed basic research across many disciplines. Along with the rapid progress in nanotechnology, the latest development in gas sensing technology is dominated by the incorporation of nanomaterials with different properties and structures. Such nanomaterials provide a variety of sensing interfaces operating on different principles ranging from chemiresistive and electrochemical to optical modules. Compared to thick film and bulk structures currently used for gas sensing, nanomaterials are advantageous in terms of surface-to-volume ratio, response time, and power consumption. However, designing nanostructured gas sensors for the marketplace requires understanding of key mechanisms in detecting certain gaseous analytes. Herein, we provide an overview of different sensing modules and nanomaterials under development for sensing critical gases in the mining industry, specifically for health and safety monitoring of mining workers. The interactions between target gas molecules and the sensing interface and strategies to tailor the gas sensing interfacial properties are highlighted throughout the review. Finally, challenges of existing nanomaterial-based sensing systems, directions for future studies, and conclusions are discussed.

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.