• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.61-69
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• 2000

Until now, the extraction of information on geographic features and the compilation of maps from satellite imagery has had many limitations because of its lower resolution compared to aerial photos to the recent. However, it is expected that the availability of high resolution satellite imagery whose spatial resolution is about 1m will reduce such limitations. Currently, a compilation of national-wide digital base maps is going on to construct the National Geographic Information Systems in Korea. It will be used for many application field of the social welfare. Therefore, in this study, we suggest that satellite imagery can help it and we have experimented on the possibility of detecting and interpreting geographic data using satellite imagery of various spatial resolutions. The interpretability and detectability of 46 features in 6 categories was experimented with 6 kinds of images of different resolutions. As a subsequent procedure, we have performed the fractal analysis for a quality test of the texture information. Through the fractal analysis, we could show that texture information and probability of discrimination increases as the spatial resolution of the image increases. Based on the results of this experiment, we could suggest the possibility of the renewal and construction of the National-wide Geographic Information Systems database using satellite imagery, as well as of examining appropriate spatial resolutions for objects of interest.

• Journal of the Korean Geotechnical Society
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• v.20 no.9
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• pp.47-56
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• 2004

A new fragmentation model, called the GRS (the generation model of weathered residual soils) model, was proposed in this study, This model could identify the formation of a residual soil. This model is based on the phenomena that as the soil was weathered more highly, soil particles were smaller and pores were more expanded simultaneously. The possibility of fragmentation, $P_F,$ which was based on the fractal theory, was introduced in this model. There were some fundamental notions in the GRS model that soil particles were generated as the rock is fragmented, and the fragmentation of the rock was performed step by step. The $P_F,$ of the rock was not constant at each fragmentation steps. As a result of application on the GRS model, there were more residue where $P_{Fi}s$ were small at any particle size. There was a S-shape of PSD curve at the concave shape of $P_{Fi},$ and the PSD curve goes to a gaped graded curve at the convex shape of $P_{Fi}.$ The shape of PSD curve was concave in the case of small $P_{Fi}s.$ The value of $P_{Fi}$ increased with the coefficient of uniformity $(C_u)$ and the fragmentation fractal dimension $(D_r),$ but had no relation with the coefficient of gradation $(C_C)$.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• The Journal of Engineering Geology
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• v.20 no.2
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• pp.183-189
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• 2010

Fault gouge samples were collected from the fault cores of the boundary faults between the Cretaceous Basement and the Tertiary Waeup Basin. Fractal dimensions (D) were obtained by using survivor grains which were analysed from six thin sections of the gouges under the optical microscope. The elliptical survivor grains show a shape preferred orientation almost parallel to clay foliation in matrix, suggesting that it was formed by the rotation of the survivor grains in abundant fine-grained matrix during repeated fault slips. The size distributions of the survivor grains follow power-laws with fractal dimensions in the 2.40-3.02 range. D values of all samples but one are higher than a specific D value equal to 2.58 which predicts the self similarity of fragmentation process in constrained comminution model (Sammis et al., 1987), which indicates large fault slip and multiple faulting. Probably the higher D values than 2.58 mean the non-self-similar evolution of cataclastic rocks where fragmentation mechanism changed from constrained comminution to the grain abrasion accompanying selective fracture of larger grains.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Geomechanics and Engineering
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• v.9 no.5
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• pp.619-629
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• 2015

Influenced by various mining activities, fractures in rock masses have different densities, set numbers and lengths, which induce different mechanical properties and failure modes of rock masses. Therefore, precisely expressing the failure criterion of the fractured rock influenced by coal mining is significant for the support design, safety assessment and disaster prevention of underground mining engineering subjected to multiple mining activities. By adopting PFC2D particle flow simulation software, this study investigated the propagation and fractal evolution laws of the micro cracks occurring in two typical kinds of rocks under uniaxial compressive condition. Furthermore, it calculated compressive strengths of the rocks with different confining pressures and box-counting dimensions. Moreover, the quantitative relation between the box-counting dimension of the rocks and the empirical parameters m and s in Hoek-Brown strength criterion was established. Results showed that with the increase of the strain, the box-counting dimension of the rocks first increased slowly at the beginning and then exhibited an exponential increase approximately. In the case of small strains of same value, the box-counting dimensions of hard rocks were smaller than those of weak rocks, while the former increased rapidly and were larger than the latter under large strain. The results also presented that there was a negative correlation between the parameters m and s in Hoek-Brown strength criterion and the box-counting dimension of the rocks suffering from variable mining activities. In other words, as the box-counting dimensions increased, the parameters m and s decreased linearly, and their relationship could be described using first order polynomial function.

• Geomechanics and Engineering
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• v.16 no.1
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• pp.85-95
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• 2018

Water affects the mechanical properties of coal and stress wave propagation. To comprehensively investigate the effect of water content on the properties of coal, laboratory tests including X-Ray Diffraction (XRD) analysis, P-wave test, S-wave test, static and dynamic compression test with different water contents were conducted. The compressive strength, elastic modulus and failure strain and their mechanism of coal specimen under coupled static-dynamic load with the increased water content were observed. Meanwhile, energy transmission and dissipation characteristics of a stress wave in coal specimens with different water contents under dynamic load and its relation with the failure features, such as fragmentation and fractal dimension, of coal was analyzed. Furthermore, the dynamic interpretation of water infusion to prevent coal burst based on water infusion model of coal seam roadway was provided.

• Journal of Ecology and Environment
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• v.29 no.5
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• pp.445-452
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• 2006

The number of species and forest area has decreased as urbanization is progressed. The landscape degradation was examined by analyzing vegetation map, satellite image and characteristics of actual vegetation. The study was conducted in Mt. Baekja and its surroundings located on Gyeongsan city, southeastern Korea. As the result of landscape analysis, agricultural field was a characteristic attribute of the study area. Lowlands of this study area were occupied by agricultural field and various plantations. For 15 years from 1987 to 2002, forest area decreased from 2,072.9 ha to 1,853.2 ha, and shape index and fractal dimension of vegetation patches increased from 1.32 to 1.65 and from 1.05 to 1.09, respectively. Pinus densiflora Siebold & Zucco. community showed the highest species diversity, whereas Larix kaempferi (Lamb.) Carriere community showed the lowest species abundance. As forest management implications, monitoring of endangered plant species (Jeffersonia dubia (Maxim.) Benth. & Hook.f. ex Baker & S.Moore), and restoration of lowland forest from plantation to natural forest were discussed. Further, establishment of greenways utilizing existing streams, roadside, and public facilities were recommended.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.6
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• pp.965-973
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• 2021

The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.121-130
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• 2024

Many foundation projects are built on red-bed soft rocks, and the damage evolution of this kind of rocks affects the safety of these projects. At present, there is insufficient research on the damage evolution of red-bed soft rocks, especially the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation. Therefore, based on the dual-porosity characteristics of pores and fissures in soft rock, we adopted a cellular automata model to simulate the propagation of these voids in soft rocks under an external load. Further, we established a macro-mesoscopic damage model of red-bed soft rocks, and its reliability was verified by tests. The results indicate that the relationship between the number and voids size conformed to a quartic polynomial, whereas the relationship between the damage variable and damage porosity conformed to a logistic curve. The damage porosity was affected by dual-porosity parameters such as the fractal dimension of pores and fissures. We verified the reliability of the model by comparing the test results with an established damage model. Our research results described the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation and provided a theoretical basis for the damage evolution of these rocks.