Diesel particulate matter dispersion analysis in underground metal/nonmetal mines using computational fluid dynamics

Diesel Particulate Matter (DPM) is a natural by-product from operating diesel engines. Since diesel power is a major source of energy for mining operations today, the adverse health effects of DPM are of a great concern. To thoroughly resolve DPM problems, it is critical that DPM propagation characteristics be understood to arrive at a sensible and practical method for addressing DPM-related issues. To achieve this, a computational fluid dynamics (CFD) method is used to simulate DPM dispersion and to predict its concentration distribution. Industrial field studies were reconstructed to evaluate the possibility of different CFD models. Experiments were also carried out in the Missouri University of Science and Technology (MISSOURI S&T) Experimental Mine to validate the selected CFD model. Based on the verified CFD model, the DPM dispersion pattern in both a straight entry and a dead-end entry were studied. The effect of variables (for example, different mining operations, inclination of dead-end entry, buoyancy effects, orientation of the tailpipe and a vehicle\u27s motion) on DPM distribution were systematically simulated to reveal high DPM regions in similar real mining scenarios. Different main airflow speeds, diesel particulate filter (DPF), and local ventilation devices were evaluated for effectiveness in clearing the DPM plume. This research can provide a means for identifying high DPM-level areas which can be used in miner health and safety training. It can also improve the understanding of the impacts of various control measures on DPM distribution which can result in an objective decision-making scheme for mining engineers to choose individual or a combination of control strategies to upgrade a miner\u27s working environment --Abstract, page iii

Patterns of stress and strain distribution during deep mining at Boulby, N. Yorkshire

The understanding of stress-deformation state transmission within the rock mass above deep mine workings is a key factor to the comprehension of the response of rock masses to changes of stress regime brought about by the mining activity for the safety of surface and subsurface structures. Based on monitoring data from active actual mine workings, this study numerically analyzes factors controlling stress and deformation using the 2D Fast Lagrangian Analysis of Continua (FLAC 2D) code and a strain-softening model to approximate creep behaviour of rock masses. The results show that distribution of stress and deformation at Boulby mine is primarily governed by the lithological heterogeneity of the overlying strata and the geological structure, including its nature within the undermined area. Data from a bespoke roof-to-floor monitoring closuremeter indicate that convergence of openings is a function of local variables, including the site location, geometry and age of the site. Patterns of ground subsidence are compared to the pattern of levelling-based measured ground subsidence. Furthermore, the analysis shows that the strain-softening model reasonably approximates the creep behaviour of the excavations. The results have implications for how we monitor and model subsidence due to mining deep excavations

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we consider weighted combinations, even for linear combinations. Hence, an obvious question is whether we can automate the search for interesting patterns and visualizations. In this paper, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. For example, to understand the distribution of crime rates in different geographic areas in terms of other (numerical, ordinal and/or categorical) variables that describe the areas. We introduce a method to find subgroups in the data that are maximally informative (in the formal Information Theoretic sense) with respect to a single or set of real-valued target attributes. The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes. The approach is based on the Subjective Interestingness framework FORSIED to enable the use of prior knowledge when finding most informative non-redundant patterns, and hence the method also supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio

Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation

Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D

Learning what matters - Sampling interesting patterns

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.Comment: PAKDD 2017, extended versio

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree