• Economic and Environmental Geology
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• v.36 no.6
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• pp.501-510
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• 2003

Several geophysical surveys(electrical resistivity, electromagnetic, seismic refraction, CPR) were conducted to primarily investigate the gallery and the geomembrane at an abandoned mine(Imcheon mine). The subsurface structure mapped from seismic refraction survey mainly consists of three velocity layers(>1000 m/s, 1000∼2000 m/s,＜2000 m/s). Top of the bedrock, whose velocities exceed 2000 m/s, appears to be at depth of 7.5∼10m. Higher resistivities (of ten thousands-hundred of thousands ohm-m) are interpreted to be associated with a open(cavities) gallery. The events at depth of approximately 0.5∼0.7m in GPR sections are probably caused by high-density-poly-ethylene geomembrane. Taking into consideration of the differences in the spatial resolution between georadar and electrical surveys, the events of geomembrane correspond to the top of the high resistivities at depth of about 2m. The segments, characterized with the higher conductivities in the electromagnetic data and the lower resistivities in the electrical resistivity data, are probably associated with surface water or tear zone of geomembrane.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1171-1181
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• 2014

Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.

• Economic and Environmental Geology
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• v.43 no.4
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• pp.315-332
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• 2010

All of the water and stone-dust samples with or without flocculant, in and around quarry mines, were analyzed for total concentrations of heavy metals, cyanide(CN), toxic organic compounds and organic phosphorus. Extraction experiments on stone-dust by EDTA and various pH solutions were also carried out, in order to evaluate the contaminant leaching from the long-term heaped stone-dust within quarry mines. The concentrations of $Cr^{6+}$, Hg, CN, TCE/PCE and total phosphorus in all samples (water and stone-dust) were under detection limits, confirming no environmental contamination from stone-dust in quarry mine areas. Lead and cadmium were not detected in all water samples. Copper and zinc were found in some water samples, and arsenic was detected in a few water samples. But they also showed levels much lower than the drinking water standard. Results of the extraction experiments by EDTA and pH solutions showed that Pb, Cr, Cd, Cu and Zn were leached out in less amounts or under detection limits. Arsenic was detected only at pH 3. From above results, we suggested that environmental contamination by quarry mine development is likely to be minor or negligible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.120-125
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• 2014

Conventional handheld metal detectors use a single induction coil to detect the metallic parts of explosive objects, and the detector generates an acoustic signal from its magnetic response to a metallic object so that an operator can confirm the existence of mines. Though metal detectors have very useful detection mechanisms to find mines, it is easy to cause a high false alarm ratio due to the detection of non-explosive metallic items such as cans, nails and other pieces of metal, etc. Also, because of the physical characteristic of a metal detector it is hard to detect non-metallic objects such as mines made of wood or plastic. Furthermore, the operator must move it to the left and right slowly and repeatedly to attain enough sensor signals to confirm the existence of mines using only a monotonous acoustic signal. To resolve the disadvantages of handheld detectors, many new approaches have been attempted, such as an arrayed detector and a visualization algorithm based on metal/non-metal sensor. In this paper, we introduce a visualization algorithm with a metal/non-metal complex sensor, an arrayed metal/non-metal sensor and the their testing and evaluation.

• Journal of Applied Reliability
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• v.14 no.3
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• pp.197-202
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• 2014

In the 21st century, the roles of UGV in the ground battle draw its attention and many research about how to use it is going on globally, but not many study is doing about how to measure its combat effectiveness in the battle. Basically, the effectiveness of UGV is different from its mission profile. Hence, we proposed Measures Of Effectiveness which can measure the UGV effectiveness based on five different missions such as mine detection, nbc detection, reconnaissance, rescue, and fire mission. We expect that these Measures Of Effectiveness proposed are able to contribute to increase the credibility of the study results for UGV effectiveness. We also hope that this paper can stimulate to expand the research scope and related field about UGV effectiveness in the future.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.215-220
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• 2005

In order to apply to land mine detection effectively, GPR using an optical electric field sensor as a receiver has been developed. The optical electric field sensor is very small and uses optical fiber instead of metallic coaxial cable. With the combination of these advantages and the bistatic radar system, it can be possible for an operator to measure quite flexible and safely. The sensor has been tested in stepped frequency radar system with frequency which consists of a vector network analyzer, a fixed double ridged horn antenna as transmitter. For considering effectiveness in real field, we applied impulse radar system, which consist of a digital oscilloscope and a impulse generator to produce the impulse. Detection of a PMN2 mine model was carried out by the impulse radar system at a sand pit. The PMN2 were detected clearly with sufficiently high resolution, the target contrast was almost the same while the scanning time decreased down to 1/100.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.531-542
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• 2021

Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed time-series analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2736-2754
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• 2015

Android dominates the mobile operating system market, which stimulates the rapid spread of mobile malware. It is quite challenging to detect mobile malware. System call sequence analysis is widely used to identify malware. However, the malware detection accuracy of existing approaches is not satisfactory since they do not consider correlation of system calls in the sequence. In this paper, we propose a new scheme called Artificial Neural Networks (ANNs) on Co-occurrence Matrices Droid (ANNCMDroid), using co-occurrence matrices to mine correlation of system calls. Our key observation is that correlation of system calls is significantly different between malware and benign software, which can be accurately expressed by co-occurrence matrices, and ANNs can effectively identify anomaly in the co-occurrence matrices. Thus at first we calculate co-occurrence matrices from the system call sequences and then convert them into vectors. Finally, these vectors are fed into ANN to detect malware. We demonstrate the effectiveness of ANNCMDroid by real experiments. Experimental results show that only 4 applications among 594 evaluated benign applications are falsely detected as malware, and only 18 applications among 614 evaluated malicious applications are not detected. As a result, ANNCMDroid achieved an F-Score of 0.981878, which is much higher than other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.