• Geomechanics and Engineering
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• v.22 no.3
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• pp.255-264
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• 2020

With the ongoing development of deep mining of coal resources, some coal mine dynamic disasters have exhibited characteristics of both coal-gas outbursts and rockbursts. Therefore, research is required on the mechanism of rockburst-outburst coupling disaster. In this study, the failure characteristics of coal-rock combination structures were investigated using lab-scale physical simulation experiments. The energy criterion of the rockburst-outburst coupling disaster was obtained, and the mechanism of the disaster induced by the gas-solid coupling instability of the coal-rock combination structure was determined. The experimental results indicate that the damage of the coal-rock structure is significantly different from that of a coal body. The influence of the coal-rock structure should be considered in the study of rockburst-outburst coupling disaster. The deformation degree of the roof is controlled by the more significant main role of the gas pressure and the difference in the strength between the rock body and the coal body. The outburst holes and spall characteristics of the coal body after the failure of the coal-rock structure are strongly affected by the difference in strength between the roof and the coal body. The research results provide an in-depth understanding of the mechanism of rockburst-outburst coupling disasters in deep mining.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.848-855
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• 2020

Land subsidence induced by underground coal mining leads to severe ecological and environmental problems. Arbuscular mycorrhizal fungi (AMF) have the potential to improve plant growth and soil properties. We aimed to assess the effects of AMF on the growth and soil properties of sea buckthorn under field conditions at different reclamation times. Inoculation with AMF significantly promoted the survival rate of sea buckthorn over a 50-month period, while also increasing plant height after 14, 26, and 50 months. Crown width after 14 months and ground diameter after 50 months of inoculation treatment were significantly higher than in the uninoculated treatment. AMF inoculation significantly improved plant mycorrhizal colonization rate and promoted an increase in mycelial density in the rhizosphere soil. The pH and electrical conductivity of rhizosphere soil also increased after inoculation. Moreover, after 26 and 50 months the soil organic matter in the inoculation treatment was significantly higher than in the control. The number of inoculated soil rhizosphere microorganisms, as well as acid phosphatase activity, also increased. AMF inoculation may play an active role in promoting plant growth and improving soil quality in the long term and is conducive to the rapid ecological restoration of damaged mining areas.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.421-428
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• 2018

In deep mining, the lateral deformation of strip coal pillar appears to be a new characteristic. In order to study the lateral deformation of coal-mass, a monitoring method and monitoring instrument were designed to investigate the lateral deformation of strip coal pillar in Tangkou Coalmine with the mining depth of over 1000 m. Because of without influence of repeated mining, the bedding sandstone roof is easy to break and the angle between maximum horizontal stress and the roadway is small, the maximum lateral deformation is only about 287 mm lower than the other pillars in the same coalmine. In deep mining, the energy accumulation and release cause a discontinuous damage in the heterogeneous coal-mass, and the lateral deformation of coal pillar shows discontinuity, step and mutation characters. These coal-masses not only show a higher plasticity but also the high brittleness at the same time, and its burst tendency is more obvious. According to the monitoring results and theoretical calculations, the yield zone of the coal pillar width is determined as 15.6 m. The monitoring results presented through this study are of great significance to the stability analysis and design of coal pillar.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.167-178
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• 2019

Open-pit (OP) and underground (UG) mining are usually used to exploit shallow and deep ore deposits, respectively. When mine deposit starts from shallow subsurface and extends to a great depth, sequential use of OP and UG mining is an efficient and economical way to maintain mining productivity. However, a transition from OP to UG mining could induce significant rock movements that cause the slope instability of the open pit. Based on Yanqianshan Iron Mine, which was in the transition from OP to UG mining, a large-scale two-dimensional (2D) model test was built according to the similar theory. Thereafter, the UG mining was carried out to mimic the process of transition from OP to UG mining to disclose the triggered rock movement as well as to assess the associated slope instability. By jointly using three-dimensional (3D) laser scanning, distributed fiber optics, and digital photogrammetry measurement, the deformations, movements and strains of the rock slope during mining were monitored. The obtained data showed that the transition from OP to UG mining led to significant slope movements and deformations that can trigger catastrophic slope failure. The progressive movement of the slope could be divided into three stages: onset of micro-fracture, propagation of tensile cracks, and the overturning and/or sliding of slopes. The failure mode depended on the orientation of structural joints of the rock mass as well as the formation of tension cracks. This study also proved that these non-contact monitoring technologies were valid methods to acquire the interior strain and external deformation with high precision.

• Safety and Health at Work
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• v.14 no.2
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• pp.193-200
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• 2023

Background: International evidence shows that mining workers are at greater risk of suicide than other workers; however, it is not known whether this applies to the Australian mining sector. Methods: Using data from the National Coronial Information System, rates of suicide among male mining workers were compared to those of three comparators: construction workers, mining and construction workers combined, and all other workers. Age-standardized suicide rates were calculated for 2001-2019 and across three intervals '2001-2006', '2007-2011', and '2012-2019'. Incidence rate ratios for suicide were calculated to compare incidence rates for mining workers, to those of the three comparative groups. Results: The suicide rate for male mining workers in Australia was estimated to be between 11 and 25 per 100,000 (likely closer to 25 per 100,000) over the period of 2001-2019. There was also evidence that the suicide rate among mining workers is increasing, and the suicide rate among mining workers for the period 2012-2019 was significantly higher than the other worker group. Conclusions: Based on available data, we tentatively deduce that suicide mortality among male mining workers is of concern. More information is needed on both industry and occupation of suicide decedents in order to better assess whether, and the extent to which, mining workers (and other industries and occupations) are at increased risk of suicide.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Genomics & Informatics
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• v.1 no.2
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• pp.65-74
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• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.770-802
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• 2022

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.623-634
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• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.