A Safety-Constrained Multi-Objective Optimization Framework for Autonomous Mining Systems: Statistical Validation in Surface and Underground Environments

Using space time block coding (STBC) to enhance the communication quality in underground environments may be of great interest. In this paper, by the aid of the simple maximum likelihood algorithm at the reception, the bit error rate performance of STBC scheme is investigated on a 2× 2 MIMO system within an underground gold mine environment. Taking Rayleigh channel as a reference, the performance in the specific indoor environment is compared to the SISO case when using BPSK and QPSK modulations.

This paper presents technology applications from the autonomous mining and construction industries in tunnel and underground environments as applied to critical large diameter utility infrastructure. A self-contained inertial navigation system for the positioning and mapping of underground infrastructures is a significant development in tunnel profiling, 3D referencing and gyro/laser surveying; a key service offered by the results of this project. The underground positioning relies on a network of satellites placed to surround an area of interest, with a range of up to 2 km through soil or rock with accuracy better than 3%, enabling accurate positioning of underground assets. The robotic mapping system has the capabilities to accurately map tunnels, pipes and conduits, in detail and sequentially transfer the data collected into popular engineering CAD systems. A specialized military grade inertial referencing system (IRS) linked to multiple scanners provides high precision profiling while measuring roughness, deflection, ovality and positioning. The IRS component is linked to multiple laser scanners supplying high precision profiling while being driven forward. Laser scanning collects hundreds of data points per second linked to an accurate position through the IRS. All data is collected to on-board computer hard drives and transferred to the engineering office via memory storage systems or directly by wireless networks set up within the pipeline. Combining sectional scans with positioning and altitude data in real time creates 3D maps for surface referencing, a valuable service for pinpointing underground infrastructure problem locations in relation to surface features enabling informed risk management decisions.

Mapping utility infrastructure via underground GPS positioning with autonomous telerobotics

The diffusion behavior law of respirable dust at fully mechanized caving face in coal mine: CFD numerical simulation and engineering application

The validity of research study results is always of concern because validity addresses the issue of the truth or falsity of the hypotheses tested. At the conclusion of a study after the statistical analyses have been completed, it is desirable to have a high level of confidence that the results approximate the truth about the relationship between the independent and dependent variables studied. The reliability (or unreliability) of measurements of the independent and dependent variables has a direct impact on the validity of study results because unreliable measures decrease statistical conclusion validity. When statistical conclusion validity is compromised, one cannot make highly valid inferences based on the results of statistical tests. Statistical conclusion validity is one of the four major types of study validity that need to be addressed within a research study. (The other types of validity include internal validity, external validity, and construct validity of putative causes and effects.) Campbell and Stanley (1963) and Cook and Campbell (1979) have published classic works that discuss the various types of validity that need to be carefully considered within the context of a research study, and I highly recommend a careful reading of them. There are three measurement-related threats to statistical conclusion validity. These are: 1. Unreliable measures, 2. Unreliable treatment implementation within an experimental or quasi-experimental design, and 3. Random irrelevancies in the experimental setting. Let us consider the role of unreliable measures as a threat to statistical conclusion validity. Tests of statistical hypotheses depend upon covariation between independent and dependent variables for inferring relationships or cause, and test the question: "Are the presumed independent and dependent variables related?" (Cook & Campbell, 1979, p. 37). When measurement of any of the variables in a hypothesized relationship is unreliable, then false conclusions about covariation of variables can be made based on statistical evidence. In other words, unreliable measures introduce more random error into scores and into the tested relationship between variables. Random error results in data "instability." Unstable sample data on any of the variables tested in a statistical hypothesis can result in spuriously high or low sample means or variation between group scores, which can lead to statistical results that can lead to false conclusions about population covariation. In essence, conclusions can be drawn based on the results of statistical analysis that are not true because measures with low reliability are unlikely to register true scores. Of course the main way to control for unreliability of measures is to carefully select measures that have prior evidence for their reliability in the population that will be used for the study. In addition, reliability should be assessed using data collected for the present study whenever possible to get an assessment of the approximate amount of random error variance. Internal consistency analysis can be conducted on questionnaires with scaled items or Likert-type items, and test-retest analysis can be done on a subsample of subjects within the study for variables that should remain stable over time. When the independent variable in a study is a treatment intervention, there is the potential for a threat to statistical conclusion validity due to a poorly standardized and controlled intervention protocol. Reduced standardization of an intervention is more likely to result when more than one person is delivering the intervention to subjects because different interveners are likely to deliver the intervention differently. …

This paper discusses the implementation of adaptability in environments that are based on the standard reference model for intelligent multimedia presentation systems. This adaptability is explored in the context of style sheets, which are represented in such formats as DSSSL. The use of existing public standards and tools for this implementation of style sheet-based adaptability is described. The Berlage environment is presented, which integrates these standards and tools into a complete storage-to-presentation hypermedia environment. The integration of the SRM into the Berlage environment is introduced in this work. This integration illustrates the issues involved in implementing adaptability in the model.

The standard reference model in the AIMI and TEXPLAN systems

Integrating the Amsterdam hypermedia model with the standard reference model for intelligent multimedia presentation systems

An analysis of COMET and MAGIC using the standard reference model for intelligent multimedia presentation systems

A standard reference model for intelligent multimedia presentation systems

Purpose This study aims to assess and enhance the production efficiency and reliability of fertilizer manufacturing plants through the application of Markovian analysis. By integrating stochastic modeling with performance management theory, the research provides a predictive performance measurement framework that links reliability metrics to key business outcomes. The research develops a probabilistic framework to model system state transitions, evaluate system availability and optimize maintenance strategies to reduce downtime and improve overall plant performance. Design/methodology/approach A discrete-time Markov chain model is constructed to represent the operational dynamics of critical units within a fertilizer plant. Historical operational and maintenance data are analyzed to develop transition probability matrices that capture the likelihood of state transitions between operational, idle and failure conditions. Key performance metrics such as steady-state probabilities, mean first passage times and recurrence times are computed to assess long-term system behavior. Statistical hypothesis testing – including paired t-tests, Wilcoxon signed-rank tests and chi-square tests – is employed to validate improvements in reliability and efficiency following the implementation of Markov-based maintenance strategies. Regression analysis is also conducted to examine the relationships between operational parameters (e.g. downtime and failure frequency) and production output. Findings The analysis reveals that the production units remain in the operational (RUN) state approximately 61.01% of the time, compared to 53% prior to optimization. Markovian-based maintenance strategies significantly reduced average weekly downtime from 42 h to 29 h (p < 0.001). Weekly production output increased from an average of 1,250 tons to 1,375 tons (p < 0.001). A chi-square test confirmed statistically significant changes in system state transitions (p < 0.001), favoring increased operational continuity. Confidence intervals constructed for key reliability parameters further strengthened the robustness of the findings. Practical implications This study provides a data-driven methodology for improving maintenance planning and production reliability in fertilizer plants. By modeling system behavior through Markovian analysis and applying statistical validation techniques, maintenance managers can develop predictive strategies that reduce unplanned downtime and enhance production efficiency. The methodology is adaptable to other continuous process industries where uptime and reliability are operational priorities and can be integrated into existing performance management systems to support data-driven decision-making and strategic alignment of maintenance activities with productivity goals. Originality/value This study offers a novel academic contribution by applying discrete-time Markov chain modeling to fertilizer manufacturing using empirical operational data. It advances performance management research by integrating stochastic modeling with statistical validation to quantify production efficiency and system reliability. The linkage between probabilistic reliability metrics (e.g. steady-state probabilities and mean first passage times) and business key performance indicators (e.g. downtime and weekly output) provides a new data-driven framework for industrial performance evaluation. This work bridges theoretical modeling with applied maintenance strategies, offering a transferable methodology relevant to researchers and practitioners seeking to optimize reliability and productivity in continuous-process industries.

With the rapid development of the 5thgeneration of wireless network communication technology, a high-speed data rate, broader bandwidth, and low-latency wireless network has already come to the truth. Meanwhile, Edge Computing which put data processing at the edge of the network has already been well developed, and it has changed the computing mode tremendously. And also, Artificial Intelligence (AI) has made a great breakthrough from Computer Vision, Unmanned Vehicle to Natural Language Processing. Nevertheless, with the increasing model size and model depth, the training data set and communication delay has become the bottleneck of AI popularization. As zillions of bytes of data are being generated at the network edge, and also lots of AI applications are being utilized by enormous customers lied at the edge, the combination of Edge Computing and AI is imperative. This paper has done a deep investigation into the new field about the confluence of Edge Computing and AI, aiming of discussing the concept, architecture, and research ideas on Edge Intelligence. To the end, this paper provides the road-map for future researching work.

Mine safety monitoring system (MSMS) can achieve a variety of safety factors of production and underground environment such as gas, temperature, humidity etc. for monitoring mine production, safety management and for safety of the miners to provide a good basis for decision making. The number of person injuries and deaths caused by the lack of information in mine are increasing year by year, so it is very important to control the accidents for achieving the safety in process of mine, and the development of the mine industry. At present, the mine monitoring system is generally composed of the monitoring sensor parameters in underground substation, information transmission system and surface base station centre. The communication between the underground substations with the surface centre consists of the information transmitting system that directly effect on the transmission quality of information and investment cost of the system using the Zigbee technology. The purpose of this work is to implement a safety system in mine based on wireless sensor network.

Though mining remains a vital shiftwork industry for U.S. commerce, problems of continued prevalence of mineworker fatigue and its mitigation persist. Publications and reports on fatigue in mining appear to be rich and diverse, yet variable and remote, much like the industry itself. The authors engaged in a brief nonexhaustive overview of the literature on sleep and fatigue among mineworking populations. This overview covers: potential sources of fatigue unique to mine work (e.g., monotonous and disengaging Work Tasks, underground environments and light exposure, remote work operations); evaluation of mitigation strategies for mineworker fatigue or working hours (e.g., shift-scheduling and training); and areas for future research and practice (e.g., fatigue risk management systems in mining, mineworker sleep and fatigue surveillance, lighting interventions, and automation). Fatigue continues to be a critical challenge for the mining industry. While research on the problems and solutions of mineworker fatigue has been limited to date, the future of fatigue research in mining can expand these findings by exploring the origins, nature, and outcomes of fatigue using advancements in lighting, automation, and fatigue risk management.

Autonomous mining is promising to address several current issues in the mining sector, such as low productivity, safety concerns, and labor shortages. Although partial automation has been achieved in some mining operations, fully autonomous mining remains challenging due to its complexity and scalability in field environments. Here we propose an autonomous mining framework based on the parallel intelligence methodology, employing self-evolving digital twins to model and guide mining processes in the real world. Our framework features a virtual mining subsystem that learns from simulating real-world scenarios and generates new ones, allowing for low-cost training and testing of the integrated autonomous mining system. Through initial validation and extensive testing, particularly in open-pit mining scenarios, our framework has demonstrated stable and efficient autonomous operations. We’ve since deployed it across more than 30 mines, resulting in the extraction of over 30 million tons of minerals. This implementation effectively eliminates the exposure of human operators to hazardous conditions while ensuring 24-hour uninterrupted operation.