• Journal of the Korean earth science society
• /
• v.43 no.3
• /
• pp.446-463
• /
• 2022

This study suggests the need for polar literacy education as an effective conceptual system to explain climate change in terms of science education in line with the common effort of humankind to respond to global environmental changes. To this end, we investigated the status of polar literacy in high school students through quantitative tests and qualitative interviews and discussed the resulting implications. A total of 329 high school sophomore students from two high schools participated in a test consisting of 25 true and false questions developed by referring to the Polar Literacy Principles, while 13 students agreed to be interviewed. The results showed that a somewhat insufficient understanding and conceptual gaps appeared regarding several areas of the Polar Literacy Principles. Knowledge of the geographic features of the polar regions was weak, and little was known about the components and key characteristics of the cryosphere. The lack of understanding of these concepts results in the inability of students to link the operational mechanisms of polar and global climate change sufficiently. While accepting unsatisfactory concepts in the school curriculum without criticism from outside media, students perceived the mechanism of climate change as somewhat monotonous or distorted. Moreover, linguistic information, analogies, and visual observation were used as cognitive strategies to compensate for the ambiguous understanding of polar and climate change. Based on the abovementioned results, we argue that polar literacy education should be introduced as a new knowledge system that can be used to aid a systematic and comprehensive understanding of climate change within the school science curriculum. Additionally, we suggest the following implications: review the consistency of knowledge related to polar literacy in other subjects, provide critical standards for out-of-school media information related to climate change, examine students' misconceptions, and identify improved thinking strategies.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1663-1676
• /
• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.728-737
• /
• 2021

In the meantime, broadcasting CG has gone through the process of dismantling, changing, and distorting, while broadcasting CG in broadcasting programs utilizes the expanded background of 'temporality' and 'formality'. This is to create an audiovisual language that appeals to human synesthesia by expressing the meaning to be conveyed in three dimensions. Broadcast CG goes beyond simple instructional and informational broadcast graphic operation, and increases the pure aesthetic value and sensibility of the video considering readability and formativeness, and through this, the audiovisual information perfection of the broadcast program is derived and acts as a very important factor. Therefore, this paper examines the results of broadcast CG production and utilization methods at existing local broadcasters, and identifies the limitations of local broadcasters' CG production and utilization through case analysis for each broadcast program type. We want to derive a model that is a compromise line. In addition, I would like to suggest a plan that can be applied more actively and practically to local broadcasting programs. In order to solve this problem, this study first examines "Analysis of cases of use of broadcasting CG production in broadcasting programs" and then "more efficient broadcasting CG production techniques by identifying problems in broadcasting CG production methods and utilization of local broadcasters" and how to actively use it". In addition, the results of this study are expected to contribute to the establishment of a new role and practical broadcast CG production model for broadcast graphic designers in charge of broadcast CG production and the technical perspective of broadcast program production by local broadcasters.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.6
• /
• pp.452-460
• /
• 2021

As the global electric vehicle (EV) market expands, eco-friendly EV that complement performance and safety problems continue to be released and the market is growing. However, in the case of EVs, the inconvenience of charging, safety problems such as electric shock, and electromagnetic interference (EMI) problems caused by the interlocking of various electronic components are problems that must be solved in EVs. The use of wireless power transmission technology can solve the problem of safety by not dealing with high current and high voltage directly and solving the inconvenience of charging EVs. In this paper, in order to reduce EMI a wireless charging control module, which is a key electronic component of WPT of EV. EMI reduction was designed through simulation of problems such as resonance and impedance that may occur in the power supply and signal distortion between high-speed communication that may occur in the signal part. Therefore, through the EMI reduction design with power integrity and signal integrity, the WPT wireless charging control module for electric vehicles reduces 10 dBu V/m and 15 dBu V/m, respectively, in 800 MHz to 1 GHz bands and 1.5 GHz bnad.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.6
• /
• pp.41-49
• /
• 2009

In this paper, the multi-camera calibration algorithm for optical motion capture system is proposed. This algorithm performs 1st camera calibration using DLT(Direct linear transformation} method and 3-axis calibration frame with 7 optical markers. And 2nd calibration is performed by waving with a wand of known length(so called wand dance} throughout desired calibration volume. In the 1st camera calibration, it is obtained not only camera parameter but also radial lens distortion parameters. These parameters are used initial solution for optimization in the 2nd camera calibration. In the 2nd camera calibration, the optimization is performed. The objective function is to minimize the difference of distance between real markers and reconstructed markers. For verification of the proposed algorithm, re-projection errors are calculated and the distance among markers in the 3-axis frame and in the wand calculated. And then it compares the proposed algorithm with commercial motion capture system. In the 3D reconstruction error of 3-axis frame, average error presents 1.7042mm(commercial system) and 0.8765mm(proposed algorithm). Average error reduces to 51.4 percent in commercial system. In the distance between markers in the wand, the average error shows 1.8897mm in the commercial system and 2.0183mm in the proposed algorithm.

• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.85-91
• /
• 2021

School violence has long been recognized as a social problem, and various efforts have been made to prevent it. In this study, we propose a system that can prevent school violence by analyzing data on the frequency of conversations between students, and identify peer relationships. The frequency of conversations between students in the class was quantified using a rating scale questionnaire, and this data was grouped into the appropriate number of clusters using the K-means algorithm. Additionally, the homeroom teacher observed the frequency and nature of conversations between students, and targeted specific individuals or groups for counseling and intervention, with the aim of reducing school violence. Data analysis revealed that the teachers' qualitative observations were consistent with the quantified data based on student questionnaires, and therefore applicable as quantitative data towards the identification and understanding of student relationships within the classroom. The study has potential limitations. The data used is subjective and based on peer evaluations which can be inconsistent as the students may use different criteria to evaluate one another. It is expected that this study will help homeroom teachers in their efforts to prevent school violence by understanding the relationships between students within the classroom.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.5D
• /
• pp.635-644
• /
• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.10
• /
• pp.455-460
• /
• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.6
• /
• pp.454-464
• /
• 2023

This paper proposes a low-power 8-bit asynchronous SAR ADC with a sampling rate of 1 MS/s for sensor node applications. The ADC uses bootstrapped switches to improve linearity and applies a VCM-based CDAC switching technique to reduce the power consumption and area of the DAC. Conventional synchronous SAR ADCs that operate in synchronization with an external clock suffer from high power consumption due to the use of a clock faster than the sampling rate, which can be overcome by using an asynchronous SAR ADC structure that handles internal comparisons in an asynchronous manner. In addition, the SAR logic is designed using dynamic logic circuits to reduce the large digital power consumption that occurs in low resolution ADC designs. The proposed ADC was simulated in a 180-nm CMOS process, and at a 1.8 V supply voltage and a sampling rate of 1 MS/s, it consumed 46.06 𝜇W of power, achieved an SNDR of 49.76 dB and an ENOB of 7.9738 bits, and obtained a FoM of 183.2 fJ/conv-step. The simulated DNL and INL are +0.186/-0.157 LSB and +0.111/-0.169 LSB.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.96-110
• /
• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.