• Geomechanics and Engineering
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• v.10 no.3
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• pp.297-314
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• 2016

With the mining depth continuously increasing, gas emission behaviors become more and more complex. Gas emission is an important basis for choosing the method of gas drainage, gas controlling. Thus, the accurate prediction of gas emission is of great significance for coal mine. In this work, based on the sources of gas emission from the heading faces and the fluid-solid coupling process, we established a gas continuous dynamic emission model, numerically simulated and applied it to the engineering. The result was roughly consistent with the actual situation and shows the model is correct. We proposed the measures of reducing the excavation distance and borehole gas drainage based on the model. The measures were applied and the result shows the overproof problem of gas emission disappears. The model considered the influence factors of gas emission wholly, and has a wide applicability, promotional value. The research is of great significance for the controlling of gas disaster, gas drainage and pre-warning coal and gas outbursts based on gas emission anomaly at the heading face.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.469-479
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• 2016

Kimchi cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. In particular Kimchi cabbages in a highland area are very sensitive to the fluctuations in supply and demand. Yield variability due to growth conditions dictates the market fluctuations of Kimchi cabbage price. This study was carried out to understand the distribution of the highland Kimchi cabbage growth status in Anbandeok. Anbandeok area in Gangneung, Gangwon-do, Korea is one of the main producing districts of highland Kimchi cabbage. The highland Kimchi cabbage status map of each growth factor was obtained from unmanned aerial vehicle (UAV) imagery and field survey data. Six status maps include UAVRGB image map, normalized difference vegetation index (NDVI) distribution/anomaly map, Crop distribution map, Planting/Harvest distribution map, Growth parameter map and Growth disorder map. As a result, the highland Kimchi cabbage status maps from May 31 to Sep. 6 in 2016 were presented to show spatial variability in the field. The benefits of the highland Kimchi cabbage status map can be summarized as follows: crop growth monitoring, reference for field observations and survey, the relative comparison of the growth condition in field scale, evaluation of growth in comparison of average year, change detection of annual crops or planting areas, abandoned fields monitoring, prediction of harvest season etc.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.

• Clinical and Experimental Pediatrics
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• v.60 no.11
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• pp.353-358
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• 2017

Purpose: To develop and evaluate a simple screening tool to assess hearing loss in newborns. A derived score was compared with the standard clinical practice tool. Methods: This cohort study was designed to screen the hearing of newborns using transiently evoked otoacoustic emission and auditory brain stem response, and to determine the risk factors associated with hearing loss of newborns in 3 tertiary hospitals in Northern Thailand. Data were prospectively collected from November 1, 2010 to May 31, 2012. To develop the risk score, clinical-risk indicators were measured by Poisson risk regression. The regression coefficients were transformed into item scores dividing each regression-coefficient with the smallest coefficient in the model, rounding the number to its nearest integer, and adding up to a total score. Results: Five clinical risk factors (Craniofacial anomaly, Ototoxicity, Birth weight, family history [Relative] of congenital sensorineural hearing loss, and Apgar score) were included in our COBRA score. The screening tool detected, by area under the receiver operating characteristic curve, more than 80% of existing hearing loss. The positive-likelihood ratio of hearing loss in patients with scores of 4, 6, and 8 were 25.21 (95% confidence interval [CI], 14.69-43.26), 58.52 (95% CI, 36.26-94.44), and 51.56 (95% CI, 33.74-78.82), respectively. This result was similar to the standard tool (The Joint Committee on Infant Hearing) of 26.72 (95% CI, 20.59-34.66). Conclusion: A simple screening tool of five predictors provides good prediction indices for newborn hearing loss, which may motivate parents to bring children for further appropriate testing and investigations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1077-1086
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• 2013

Recent huge losses of both life and property have occurred by unexpected natural disasters. We studied snow damages, an important natural disaster issue because it happens more frequently in recent years. This study tries to select vulnerable areas of snowfall in advance and then establish climate change adaptation policy for minimizing unexpected snowfall damage. Busan, where is our study area, has hilly in downtown areas so that topography characteristics of the roads such as slope, elevation and aspect are vulnerable to snowfall. The sudden snowfall in Busan causes traffic jam and causes some schools in hilly to close some schools. At this moment, the adaptation policy has to be established for infrastructure (such as roads) in advance, because prediction of anomaly climate due to global warming is so difficult beside the damage of natural disaster is huge. Therefore, the purpose of this study is contribute to selecting and assessing vulnerable zones of snow damage focusing topography characteristics of the roads and then evaluating the degree of risk of vulnerable zones.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.119-126
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• 2016

Across the world, the industrialization has increased the frequency of climate anomaly. The size of damage due to recent natural disasters is growing large and fast, and the human damage and economic loss due to disasters are consistently increasing. Urbanization has a structure vulnerable to natural disasters. Therefore, in order to reduce damage from natural disasters, both hardware and software approaches should be utilized. Currently, however, the development of a statistical access process for 'analysis of disaster occurrence factor' and 'prediction of damage costs' for disaster prevention and overall disaster management is inadequate. In case of local governments, overall disaster management system is not established, or even if it is established, unscientific classification system and management lead to low utility of natural statistics of disaster year book. Therefore, in order to minimize disaster damage and for rational disaster management, the disaster damage survey process should be improved. This study selected gale as the focused analysis target among natural disasters recorded in disaster year book such as storm, torrential rain, gale, high seas, and heavy snow, and analyzed disaster survey process. Based on disaster year book, the gale damage size was analyzed and the issues occurring from the correlation of gale and damage amount were examined, so as to suggest an improvement plan for reliable natural disaster information collection and systematic natural disaster damage survey.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.509-521
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• 2011

The purpose of this study is to estimate overall reliability and applicability of the watershed modeling for systematic management of point and non-point sources via water quality analysis and prediction of runoff discharge within watershed. Recently, runoff characteristics and pollutant characteristics have been changing in watershed by anomaly climate and urbanization. In this study, the effects of watershed scale were analyzed in runoff and water quality modeling using HSPF. In case of correlation coefficient, its range was from 0.936 to 0.984 in case A(divided - 2 small watersheds). On the other hand, its range was form 0.840 to 0.899 in case B(united - 1 watershed). In case of Nash-Sutcliffe coefficient, its range was from 0.718 to 0.966 in case A. On the other hand, its range was from 0.441 to 0.683 in case B. As a result, it was judged that case A was more accurate than case B. Therefore, runoff and water quality modeling in minimum watershed scale that was provided data for calibration and verification was judged to be favorable in accuracy. If optimal watershed dividing and parameter optimization using PEST in HSPF with more reliable measured data are carried out, more accurate runoff and water quality modeling will be performed.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.189.1-189.1
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• 2011

Global warming effect was intensified due to rapid growth of fossil fuel consumption caused by urbanization and industrialization. Various efforts was being done to solve the problems leading to anomaly climate such as flood, downpour, heavy snow. As a results of international efforts for management of global warming, Kyoto Protocol, which was passed in Kyoto, Japan in 1997, designated $CO_2$, $CH_4$, $N_2O$, HFCs, PFCs, $SF_6$ as a global warming gases. And IPCC(Intergovernmental Panel on Climate Change) suggested IPCC guideline for systematic establishment of national greenhouse gas inventory. Among five categories in IPCC guideline, the representative emission source of waste category is SWDS(solid waste disposal site). The concentrative research should progress for effective management of greenhouse gas related with waste. In this study, Tier1 and Tier2 methods which was suggested by 2006 IPCC(Intergovernmental Panel on Climate Change) guideline, was used to predict methane generation from C sanitary landfill located in Chungju area. To predict methane generation from C sanitary landfill, all factors were defaults values that were provided by 2006 IPCC guideline and Korea emission factors for Tier1 and Tier2 method. And economics of generated methane was estimated. From the predicted result using IPCC guideline, the methane generation was persistingly increased over a 9-year period(2000 ~ 2008). Aggregated amount of methane generation was about 3,017ton and 3,170ton predicted by Tier1 and Tier2, respectively. From the results of estimated economic value gained by generated methane from the C sanitary landfill for ten years from now(2010 ~ 2020), the profit was about 2.39 ~ 2.76 hundred million won.