• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1545-1553
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• 2018

Recent eruptive activity at Kīlauea Volcano started on at the end of April in 2018 showed rapid ground deflation between May and June in 2018. On summit area Halema'uma'u lava lake continued to drop at high speed and Kīlauea's summit continued to deflate. GPS receivers and electronic tiltmeters detected the surface deformation greater than 2 meters. We explored the time-series surface deformation at Kīlauea Volcano, focusing on the early stage of eruptive activity, using multi-temporal COSMO-SkyMed SAR imagery. The observed maximum deformation in line-of-sight (LOS) direction was about -1.5 meter, and it indicates approximately -1.9 meter in subsiding direction by applying incidence angle. The results showed that summit began to deflate just after the event started and most of deformation occurred between early May and the end of June. Moreover, we confirmed that summit's deflation rarely happened since July 2018, which means volcanic activity entered a stable stage. The best-fit magma source model based on time-series surface deformation demonstrated that magma chambers were lying at depths between 2-3 km, and it showed a deepening trend in time. Along with the change of source depth, the center of each magma model moved toward the southwest according to the time. These results have a potential risk of including bias coming from single track observation. Therefore, to complement the initial results, we need to generate precise magma source model based on three-dimensional measurements in further research.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.83-88
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• 2022

Companies make decisions with risks such as choosing an investment plan in order to pursue profits. This study explained the decision making of the management of construction companies in South Korea using the tendency to avoid losses in the Prospect Theory. To this end, 20-year financial data of 2,881 companies engaged in real estate development, which have to bear the greatest risk among the construction industry, were collected. The collected companies were roughly classified based on the reference point, and the causal relationship between average return on equity and risk preference by group was empirically analyzed through regression analysis. As a result, it was confirmed that if the average return on equity of a company decreases for the group above the reference point, it tends to select an investment plan with low uncertainty in order not to lose additional money. In addition, it was confirmed that if the average return on equity of a company decreases for the group below the reference point, it tends to select an investment plan with high uncertainty to move to the profit area. This result is exactly consistent with the loss aversion tendency of the Prospect Theory.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.746-752
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• 2022

Kiribati, a South Pacific island, and its surrounding countries are gradually submerging to rising sea levels. The sea level continues to change according to the degree of thermal expansion of glaciers and seawater that decreases with increase in temperature. Global warming affects both the amount and volume of seawater, thus increasing sea level. Tidal phenomena occur twice a day to the attraction of celestial bodies such as the moon and the sun. The moon changes the angle of orbiting surface with the Earth equator every 18.6 years, and the magnitude of the tidal force changes depending on the distance between the Earth equator and the moon orbital surface. The University of Hawaii Sea Level Center selected Tarawa, Christmas, Kanton of Kiribati,, Lautoka, Suva of Fiji,Funafuti of Tuvalu, Nuk1u'alofa of Tonga, and Port Vila of Vanuatu. When comparing tide levels for each year for 19 years, the focus was on checking the change in sleep to Tide levels, and rising sea levels was the effect of Tide levels. The highest astronomical tides (HAT) and lowest astronomical tides (LAT) were identified as Tarawa 297.0, 50.8 cm, Christmas 123.8, 19.9 cm, Kanton 173.7, 39.9 cm, Lautoka 240.7, 11.3 cm, Funafuti 328.6, 98.4 cm, Nuk1u'alofa 188.8, 15.5 cm, Port Vila 161.5, -0.5cm, respectively. The Sea level rising speed was Tarawa 3.1 mm/year, Christmas -1.0 mm/year, Kanton 1.6 mm/year, Lautoka 3.1 mm/year, Suva 7.4 mm/year, Funafuti 1.4 mm/year, Nuk1u'alofa 4.2 mm/year, and Port Vila -1.2 mm/year, respectively

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Korea Science and Art Forum
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• v.31
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• pp.21-32
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• 2017

Local festivals form the regional cultures and atmosphere of communication; they increase the demand of domestic tourism businesses and thus, have an important role in ripple effects (e.g. regional image improvement, tourist influx, job creation, regional contents development, and local product sales) and economic revitalization. IoT (Internet of Thing) technologies have been developed especially, beacon-one of the IoT services has been applied as plenty of types and forms both domestically and internationally. However, notwithstanding expansion of current digital mobile technologies, it still remains as difficult for the individual to track the information about all the local festivals and to fulfill the tourists' needs of enjoying festivals given the weak strategic approaches and advertisement activities. Furthermore, current festival-related mobile applications don't function well as delivering information and have numerous contents issues (e.g. ways of information delivery within the festival places, independent application usage for each festival, one time usage due to one time event). This research, based on the background mentioned above, aims to develop the local festival mobile application and data analysis system applying beacon technology. First of all, three algorithms were developed, namely, 'festival crowding algorithm', 'visitor stats algorithm', and 'customized information algorithm', and then beta test was followed with the developed application and data analysis system. As a result, they could form the database of visitors' types and behaviors, and provide functions and services, such as personalized information, waiting time for festival contents, and 'hot place' function. Besides, in Google Play store, they also got the titles given with more than 13,000 downloads within first three months and as the most exposed application related with festivals; and, thus, got credited with their marketability and excellence. This research follows this order: chapter 2 shows the literature review of local festival related with technology development, beacon service, and festival application. In Chapter 3, design plans and conditions are described of developing local festival mobile application and data analysis system with beacon. Chapter 4 evaluates the results of the beta performance test to verify applicability of the developed application and data analysis system, and lastly, chapter 5 explains the conclusion and suggests the future research.