• Journal of Climate Change Research
• /
• v.1 no.2
• /
• pp.133-145
• /
• 2010

This study examined the characteristics of tropical cyclone(TC) activity over the western North Pacific(WNP) in 2009. Twenty-two TCs formed in 2009, which is slightly below normal(1979~2009 average: 25.8) and most of these occurred during the months of July to October. Most TCs in 2009 was formed over the northern Philippines and the eastern part of the WNP and they moved towards the South China Sea and the east of Japan, resulting in less TC affecting the East China Sea and Korea. The TC activity in 2009 is modulated by the large-scale circulations induced by the El $Ni{\tilde{n}}o$ and vigorous convection activity over the WNP. As the general characteristics of El $Ni{\tilde{n}}o$ year, the difference in sea surface temperature between the central Pacific and the eastern Pacific causes an anomalous westerly winds, expanding the WNP monsoon trough farther eastward. Accordingly, TC formation has relatively increased in the east part of the WNP. Active convection activities over the subtropical western Pacific excite a Rossby wave propagating from the South China Sea to mid-latitudes, resulting in an anomalous easterly steering flow in the South China, anomalous northwesterly over the East China Sea and Korea, and anomalous southwesterly over the east of Japan. Summing up, the TCs cannot enter the East China Sea and Korean region and instead they move towards the South China Sea or south-east of Japan. There were no effects of TCs in Korea in 2009. It is anticipated that this study which analyzed unusual TC activity and large-scale circulations in 2009 would help the predictability of TC effects to increase according to climate change in the East Asia.

• Journal of Korean Society of Forest Science
• /
• v.108 no.1
• /
• pp.40-49
• /
• 2019

Coarse woody debris (CWD), which is a component of the forest ecosystem, plays a major role in forest energy flow and nutrient cycling. In particular, CWD isolates carbon for a long time and is important in terms of slowing the rate of carbon released from the forest to the atmosphere. Therefore, this study measured the physiochemical characteristics and respiration rate ($R_{CWD}$) of CWD for Larix kaempferi and Pinus rigida in temperate forests in central Korea. In summer 2018, CWD samples from decay class (DC) I to IV were collected in the 14 forest stands. $R_{CWD}$ and physiochemical characteristics were measured using a closed chamber with a portable carbon dioxide sensor in the laboratory. In both species, as CWD decomposition progressed, the density ($D_{CWD}$) of the CWD decreased while the water content ($WC_{CWD}$) increased. Furthermore, the carbon concentrations did not significantly differ by DC, whereas the nitrogen concentration significantly increased and the C/N ratio decreased. The respiration rate of L. kaempferi CWD increased significantly up to DC IV, but for P. rigida it increased to DC II and then unchanged for DC II-IV. Accordingly, except for carbon concentration, all the measured characteristics showed a significant correlation with $R_{CWD}$. Multiple linear regression showed that $WC_{CWD}$ was the most influential factor on $R_{CWD}$. $WC_{CWD}$ affects $R_{CWD}$ by increasing microbial activity and is closely related to complex environmental factors such as temperature and light conditions. Therefore, it is necessary to study their correlation and estimate the time-series pattern of CWD moisture.

• Korean Journal of Heritage: History & Science
• /
• v.54 no.1
• /
• pp.120-137
• /
• 2021

There were five palaces built during the Joseon Period. Gyeongbokgung Palace was the first one, founded in the 4th year of King Taejo (1395), and depending on the historical interpretation, Changdeokgung Palace, Changgyeonggung Palace, Gyeongungung Palace (Deoksugung), and Gyeongdeokgung Palace (Gyeonghuigung) were also built. The palaces represent the best architecture of the time. In addition, the palaces of the Joseon period have been rebuilt several times, so they contain the architectural history of the Joseon period over the last 500 years. In this paper, all the excavations of five palaces in the Joseon Period were surveyed, and the foundations of the buildings were analyzed. In particular, the aim of this paper is to investigate Jeoksim (foundations of buildings under cornerstone) to understand the characteristics of each palace by period. Accordingly, the changes of the construction techniques of the foundations of the palaces were studied. There are a total of 23 types of Jeoksim. All five palaces have a certain type (I~V) of construction technique, thus it was confirmed that there was a certain pattern in the method of constructing the foundations of palace buildings in the Joseon Dynasty. In addition, Jeoksim was mainly built by certain materials and construction methods (I-1) during the 14th to the 17th century, but new types of Jeoksim were built in the palaces starting from the 18th century, during the reign of King Jeongjo. In the 19th century, when King Gojong sat on the throne, the Jeoksim was built in various shapes, materials, and in 22 types of construction methods. Up to now, research on the remains of palaces were mainly conducted on the Gyeongbokgung Palace, so it was not possible to confirm the foundations of 17th-18th century buildings, where reconstruction had stopped after the Imjin War in 1592. However, through this study, it was possible to classify the transition periodsstheir features periods of palace building foundation construction from the 14th to the 20th century by comparing the remains of five palace building sites.

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