• Smart Media Journal
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• v.8 no.4
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• pp.9-16
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• 2019

In order to realize IoT(Internet of Things) service, it is necessary to manage sensing data and build a service with respect to its scalability. However, existing internet services use unique protocols and non-standardized functions for each service provider, and it is difficult to provide data management and service because they use short-range communication technology such as Bluetooth. In addition, plurality of APs and gateways must be taken into consideration in establishing a wide area network. In this paper, we propose a sensing data management system using LoRa(Long Range) communication based on Mobius platform. The end device that drives Tas is configured to collect sensing data, configure an application gateway that drives &Cube, and transmit sensing data to the server. In addition, a server that manages the Mobius is configured to handle the sensing data transmitted from the application gateway to provide a monitoring service. We establish a wide area network through LoRa communication between the end device and the gateway and provide data management and service corresponding to the internet through the Mobius platform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.829-836
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• 2019

IoT devices are widely used in network construction and are increasing. If necessary, heterogeneous IoT devices are used for data transmission. This paper proposes to prevent the method of data loss due to differences in throughput when the local network is constructed by Bluetooth 5 and long range network does by LoRa(Long Range). Data loss occur when the data transmits through LoRa, due to the throughputs of Bluetooth 5 faster than that of LoRa. The prevent method proposed by this paper can apply not only Bluetooth 5 and LoRa but heterogeneous IoT devices and expect to prevent data loss due to differences in throughput between heterogeneous IoT devices. Also, this paper shows the simulation result by applying the proposed avoid method. In this paper, two way to the preventive method shows the data transmission ratio and amount of memory that of necessity.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.152-159
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• 2007

In this paper, a new design technique on the LRF which is useful for low power laser and a CBDA(Cummulative Binary Detection Algorithm) is proposed. The LD(Laser Diode) and Si-APD(Silicon Avalanche Photo Diode) are used for saving a power. In order to prove the detection range, the Si-APD binary data are accumulated before the range computation and the range finding algorithm. A prototype of the proposed DLRF(Diode Laser Range Finder) system was made and tested. An experimental result shows that the DLRF system have the same detection range using a less power(almost 1/32) than an usual military LRF. The proposed DLRF can be applied to the Unmanned Vehicles, Robot and Future Combat System of a tiny size and a low power LRF.

• Proceedings of the KSRS Conference
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• v.1
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• pp.504-507
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• 2006

Airborn Lidar technology has been applied to diverse applications with the advantages of accurate 3D information. Further, Lidar intensity, backscattered signal power, can provid us additional information regarding target's characteristics. Lidar intensity varies by the target reflectance, moisture condition, range, and viewing geometry. This study purposes to generate normalized airborne LiDAR intensity image considering those influential factors such as reflectance, range and geometric/topographic factors (scan angle, ground height, aspect, slope, local incidence angle: LIA). Laser points from one flight line were extracted to simplify the geometric conditions. Laser intensities of sample plots, selected by using a set of reference data and ground survey, werethen statistically analyzed with independent variables. Target reflectance, range between sensor and target, and surface slope were main factors to influence the laser intensity. Intensity of laser points was initially normalized by removing range effect only. However, microsite topographic factor, such as slope angle, was not normalized due to difficulty of automatic calculation.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.23-26
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• 1999

The properties of concrete can be affected by high range water reducing agent and cement. The data for compatibility and effect of fluidity is reported already according to the mixing proportion of kinds of cements and high range water reducing agents. Moreover, the international market of construction has been opened, the international standard of capability has been promoted and the international exchange of construction materials has been brisked. This study investigated fluidity properties of mortar due to kinds of cements and high range water reducing agents which are producted in different nations. Also studied were the compatibility effect of cements and high range water reducing agents.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.52-54
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• 2007

This study has investigated the basic data of untrained boys and girls' VRP. The VRP comparison was executed between 5 boys(lO to 11 years old) and girls(10 to 11 years old). The measure of VRP was implemented by using Dr. Speech 4.0(Tiger-electronics) phonetogram program. The comparison of boys and girls' maximum and minimum range, the mean of boys' maximum range is 93.68dB(SD 7.90) and girls' range is 93.12dB(SD 5.11). There was no difference and the mean of minimum range of boy is 68.08dB(SD 3.59), girl is 71.10dB(SD 3.06).

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Korean Institute of Interior Design Journal
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• v.27 no.1
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• pp.29-38
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• 2018

This research is to analyze the change of pupil size in gender through the eye-tracking experiment in large complex cultural space. It is meaningful that figured out the common characteristics and differences from gender observation characteristics. Through this research, the analyzed results of the observation time measurement that appeared from the fixation and saccades pupil indicator able to define as follows. Firstly, it was suggested that there were differences between each gender and participants through extract pupil size that can be the standard examples for the case from male and female and the process of extracting the relative pupil size change on the hourly range. From the specific time range, it was possible to indicate bending characteristics and reversal phenomena of Fixation and Saccades. Second, the result was found equally from both male and female group that the rapid increment of pupil size at initial time range immediately after the eye-tracking experiment has been initiated. This can be considered to actively accepting the stress given by the subject through the extended pupil after 10 seconds that compare to indicated very low pupil size between 0 to 10 seconds after starting the experiment. Third, meanwhile 0 to 10 seconds after initial observation are the time of sudden change in the pupil size, therefore these time range data cannot be regarded as observed in the appropriate condition. Thus, it able to define the highest times of emotional processing for male as 10 to 80 seconds, and for female as 10 to 70 seconds. There was no definition of the time range data for observation experiment from previous research, this data can be considered to stable time to observation through the pupil extension. Therefore, it is possible to set suitable time of observation experiment to be around 70 to 80 seconds exclude initial experiment time.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.366-374
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• 2017

The objective of this study is to explore the applicability of very short-range-forecast rainfall for the early warning of mud-debris flows. An artificial neural network was applied to use the very short-range-forecast rainfall data. The neural network is learned by using the relationship between the radar and the AWS, and forecasted rainfall is estimated by replacing the radar rainfall with the MAPLE data as the very short-range-forecast rainfall data. The applicability of forecasted rainfall by the MAPLE was compared with the AWS rainfall at the test-bed using the rainfall criteria for cumulative rainfall of 6hr, 12hr, and 24hr respectively. As a result, it was confirmed that forecasted rainfall using the MAPLE can be issued prior to the AWS warning.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.