All Issue

2019 Vol.56, Issue 5 Preview Page
October 2019. pp. 435-446
Abstract


References
1 

Ballard, D.H., 1987. Modular learning in neural networks, In Proc. AAAI, p.279-284.

2 

Bengio, Y., Courville, A., and Vincent, P., 2013. Representation learning: A review and new perspectives. IEEE Trans. PAMI, special issue Learning Deep Architectures.

10.1109/TPAMI.2013.5023787338
3 

Bewley, A. and Upcroft, B., 2016. Background appearance modeling with applications to visual object detectin in an open-pit mine. J. of Field Robot, 34(1), 53-73.

10.1002/rob.21667
4 

Bishop, C.M., 2007. Pattern Recognition and Machine Learning, Springer.

5 

Boulle, M., 2018. Predicting dangerous seismic events in coal mines under distribution drift. in Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), p.221-224.

6 

Choi, Y., 2017. The roles and technology trends of ICT in mines. J. of the Korea Soc. of Min. and Ener. Resour, 54(1), 66-78.

10.12972/ksmer.2017.54.1.066
7 

Du, S., Feng, G., Wang, J., Feng, S., Malekian, R., and Li, Z., 2019. A new machine-learing prediction model for slope deformation of an open-pit mine: an evaluation of field data. Energies, 12, 1288-1302.

10.3390/en12071288
8 

ECMiner, 2019. ECMinerIMS, ecminer.com/?page_id=177, accessed at 19 August, 2019.

9 

Ford, A. and Blenkinsop, T.G., 2008. Evaluating geological complexity and complexity gradients as controls on copper mineralization, Mt Isa Inlier. Austral. J. of Earth Sci, 55, 13-23.

10.1080/08120090701581364
10 

Geng, Y., Su, L., Jia, Y., and Han, C., 2019. Seismic events prediction using deep temporal convolution networks. J. of Elec. and Comp. Eng, 2019, p.14, Article ID 7343784.

10.1155/2019/7343784
11 

Guo, H., Zhou, J., Koopialipoor, M., Armaghani, D.J., and Tahir, M.M., 2019. Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng. w Comp, Published online:

10.1007/s00366-019-00816-y.
12 

Hinton. G.E., 2007. Learning multiple layers of representation. Trends in Cognitive Sciences, 11, 428-434.

10.1016/j.tics.2007.09.00417921042
13 

Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neur. Comp, 9(8), 1735-1780.

10.1162/neco.1997.9.8.17359377276
14 

INFINITT, 2018, www.mountainsidehosp.com/assets/39/7/ Infinitt_-_Clinician_PACS_Guide.pdf, Accessed at 19, August, 2019.

15 

Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H.A., and Kumar, V., 2018. Machine learning for the geosciences: challenges and opportunities. IEEE Trans. Knowl. Data Eng. Published online:

10.1109/TKDE.2018. 2861006.
16 

LeCun, Y., 1989. Backpropagation applied to handwritten zip code recognition. Neur. Comp, 1, 541-551.

10.1162/neco.1989.1.4.541
17 

LeCun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521, 436.

10.1038/nature1453926017442
18 

Lee, C., Kim, S.-M., and Choi, Y., 2019. Case analysis for introduction of machine learning technology to the mining industry. Tunn. and Undg. Space, 29(1), 1-11.

19 

Li, S., Chen, J., and Xiang, J., 2019. Application of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data. Nuer. Comp. and App, Published online:

10.1007/s00521- 019-04341-3.
20 

Lim, H.J., 2017. Development direction of fraud detection ststem technology. J. of the Korea Ins. of Comm. Sci, 34(3), 37-46.

21 

Luo, X., Lin, F., Zhu, S., Yu, M., Zhang, Z., Meng, L., and Peng, L., 2019. Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors. PLoS ONE, 14(4), Published online:

10.1371/journal.pone.0215134.
22 

Marek, S. and Lukasz, W., 2010. Application of rule induction al-gorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Archives of Ming Sciences, 55, 91-114.

23 

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Reidmiller, M, 2013. Playing Atari with Deep Reinforcement Learning, arXiv:1312.5602.

24 

Nguyen, H., Bui, X.-N., Bui, H.-B., and Mai, N.-L., 2018. A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine. Neur. Comp. and App, Publiched online: https:// doi.org/10.1007/s00521-018-3717-5.

10.1007/s00521-018-3717-5
25 

Oracle, 2018. What's the Difference Between AI, Machine Learning, and Deep Learning?, https://blogs.oracle.com/ bigdata/difference-ai-machine-learning-deep-learning, accessed at 19, August, 2019.

26 

TechTarget, 2018. big data analytics, searchbusinessanalytics. techtarget.com/definition/big-data-analytics, Accessed at 19, August 2019.

27 

Tessema, A., 2017. Mineral systems analysis and artificial neural network modeling of chromite prospectivity in the western limb of the bushveld complex. South Africa, Nat. Resour. Resch. 26(4), 465-488.

10.1007/s11053-017-9344-5
28 

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.A., 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. of Mach. Learn. Resch, 11, 3371-3408.

29 

Williams, R.J., Hinton, G.E., and Rumelhart, D.E., 1986. Learning representations by back-propagating errors. Nature, 323(6088), 533-536.

10.1038/323533a0
30 

Xiong, W., Ji, X., Ma, Y., Wang, Y., AlBinHassan, N.M., Ali, M.N., and Luo, Y., 2018a. Seismic fault detection with convolution neural network. Geophysics, 83(5), O97-O103.

10.1190/geo2017-0666.1
31 

Xiong, Y. and Zuo, R., 2016. Recognition of geochemical anomalies using a deep autoencoder network. Comp & Geosci, 86, 75-82.

10.1016/j.cageo.2015.10.006
32 

Xiong, Y., Zuo, R., and Carranza, E.J.M., 2018b. Mapping mineral prospectivity through big data analytics and a deep learning algorithm. Ore Geology Reviews, 102, 811-817.

10.1016/j.oregeorev.2018.10.006
33 

Yinka-Banjo, C., Bagula, A., and Osunmakinde, I.O., 2012. Autonomous multi-robot behaviours for safety inspection under the constraints of underground mine Terrains. Ubiq. Comp. and Comm. J., 7(5), 1316-1328.

34 

Zhang, C., Fu, Y., Deng, F., Wei, B., and Wu, X., 2018. Methane gas density monitoring and predicting based on RFID sensor tag and CNN algorithm. Electronics, 7(5), 69-81.

10.3390/electronics7050069
35 

Zhang, S., Xiao., K., Carranza, E.J.M., Yang, F., and Zhao, Z., 2019. Integration of auto-encoder network with density- based spatial clustering for geochemical anomaly detection for mineral exploration. Comp & Geosci, 130, 43-56.

10.1016/j.cageo.2019.05.011
36 

Zuo, R., Xiong, Y., Wang, J., and Carranza, E.J.M., 2019. Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1-14.

10.1016/j.earscirev.2019.02.023
Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 56
  • No :5
  • Pages :435-446
  • Received Date :2019. 09. 16
  • Revised Date :2019. 09. 25
  • Accepted Date : 2019. 10. 25