• Economic and Environmental Geology
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• v.55 no.1
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• pp.63-76
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• 2022

Dacitic tuffs, 97 to 118 m thick, were recovered from the lower part of the subsurface Seongdongri Formation, Janggi Basin, which was drilled to assess the potential for underground storage of carbon dioxide. The tuffs are divided into four depositional units(Unit 1 to 4) based on internal structures and particle componentry. Unit 1 and Units 3/4 are ignimbrites that accumulated in subaerial and subaqueous settings, respectively, whereas Unit 2 is braided-stream deposits that accumulated during a volcanic quiescence, and no dacitic tuff is observed. A series of analysis shows that mordenite and clinoptilolite mainly fill the vesicles of glass shards, suggesting their formation by replacement and dissolution of volcanic glass and precipitation from interstitial water during burial and diagenesis. Glass-replaced clinoptilolite has higher Si/Al ratios and Na contents than the vesicle-filling clinoptilolite in Units 3. However, the composition of clinoptilolite becomes identical in Unit 4, irrespective of the occurrence and location. This suggests that the Si/Al ratio and pH in the interstitial water increased with time because of the replacement and leaching of volcanic glass, and that the composition of interstitial water was different between the eastern and western parts of the basin during the formation of the clinoptilolite in Units 1 and 3. It is also inferred that the formation of the two zeolite minerals was sequential according to the depositional units, i.e., the clinoptilolite formed after the growth of mordenite. To summarize, during a volcanic quiescence after the deposition of Unit 1, pH was higher in the western part of the basin because of eastward tilting of the basin floor, and the zeolite ceased to grow because of the closure of the pore space as a result of the growth of smectite. On the other hand, clinoptilolite could grow in the eastern part of the basin in an open system affected by groundwater, where braided stream was developed. Afterwards, Units 3 and 4 were submerged under water because of the basin subsidence, and the alkali content of the interstitial water increased gradually, eventually becoming identical in the eastern and western parts of the basin. This study thus shows that volcanic deposits of similar composition can have variable distribution of zeolite mineral depending on the drainage and depositional environment of basins.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.