• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1209-1216
• /
• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Journal of Navigation and Port Research
• /
• v.46 no.3
• /
• pp.243-250
• /
• 2022

The purpose of this study was to propose technology to detect the wave in the image in real-time, and calculate the height of the wave-overtopping through image analysis using artificial intelligence. It was confirmed that the proposed wave overtopping detection system proposed in this study could detect the occurring of wave overtopping, even in severe weather and at night in real-time. In particular, a filtering algorithm for determining if the wave overtopping event was used, to improve the accuracy of detecting the occurrence of wave overtopping, based on a convolutional neural networks to catch the wave overtopping in CCTV images in real-time. As a result, the accuracy of the wave overtopping detection through AP50 was reviewed as 59.6%, and the speed of the overtaking detection model was 70fps based on GPU, confirming that accuracy and speed are suitable for real-time wave overtopping detection.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.8
• /
• pp.33-41
• /
• 2024

The continuous rise in the number of multicultural households and the issue of insufficient Korean language proficiency among marriage immigrants have highlighted the need to expand support programs for multicultural families and the importance of staffing multicultural centers. This paper designs and implements a diary application that leverages AI technology to enhance communication between parents and children in multicultural families based on diary entries. The proposed technology uses OCR, machine translation, Korean language correction, and sentiment analysis AI models to facilitate diary-based conversations between parents and children, addressing linguistic barriers and fostering emotional bonds. Additionally, it aims to provide direction for the development and harmony of future multicultural societies.

• Therapeutic Science for Rehabilitation
• /
• v.12 no.4
• /
• pp.9-22
• /
• 2023

The Lifestyle-DEPER (Decision, Execution, Personal Factors, Environment, Resources) model explains lifestyle formation. Lifestyles are shaped through the decision, execution, and habituation stages. Factors influencing the establishment of a lifestyle are categorized as environmental, resource, and personal. The environment encompasses our surroundings and social, physical, cultural, and virtual environments. Resources refer to what individuals possess, such as health, time, economic, and social resources. Personal factors include competencies, needs, and values. At the lifestyle establishment stage, each of these factors influences a different stage. These collective processes are referred to as events, encompassing both personal and social events. Health-related lifestyle factors include physical activity, nutrition, social relationships, and occupational participation. These are the goals of lifestyle intervention. The intervention strategy based on the Lifestyle-DEPER model, called KEEP (Knowledge, Evaluation, Experience, Plan), is a comprehensive approach to promoting a healthy lifestyle by considering lifestyle formation stages and their influencing factors. This study introduces the Lifestyle-DEPER model and presents a lifestyle intervention strategy (KEEP) to promote health. Further research is required to validate the practicality of the model after applying interventions based on the lifestyle construction model.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.6
• /
• pp.679-686
• /
• 2020

In this thesis, an enhanced method for the feature extraction of vocal source signals and score combination using an MCE-Based weight estimation of the score of multiple feature vectors are proposed for the performance improvement of speaker recognition systems. The proposed feature vector is composed of perceptual linear predictive cepstral coefficients, skewness, and kurtosis extracted with lowpass filtered glottal flow signals to eliminate the flat spectrum region, which is a meaningless information section. The proposed feature was used to improve the conventional speaker recognition system utilizing the mel-frequency cepstral coefficients and the perceptual linear predictive cepstral coefficients extracted with the speech signals and Gaussian mixture models. In addition, to increase the reliability of the estimated scores, instead of estimating the weight using the probability distribution of the convectional score, the scores evaluated by the conventional vocal tract, and the proposed feature are fused by the MCE-Based score combination method to find the optimal speaker. The experimental results showed that the proposed feature vectors contained valid information to recognize the speaker. In addition, when speaker recognition is performed by combining the MCE-based multiple feature parameter scores, the recognition system outperformed the conventional one, particularly in low Gaussian mixture cases.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.785-791
• /
• 2020

The radiance measured from satellite has noise due to atmospheric effect. Atmospheric correction is the process of calculating surface reflectance by removing atmospheric effect and surface reflectance is calculated by the Radiative Transfer Model (RTM)-based Look-Up Table (LUT). In general, studies using a LUT make LUT for each channel with the same atmospheric and geometric conditions. However, atmospheric effect of atmospheric factors do not react sensitively in the same channel. In this study, the LUT for each channel of Korea Multi-Purpose SATellite (KOMPSAT)-3/3A was made under the same atmospheric·geometric conditions. And, the accuracy of the LUT was verified by using the simulated Top of Atmosphere radiation and surface reflectance in the RTM. As a result, the relative error of the surface reflectance in the blue channel that sensitive to the aerosol optical depth was 81.14% at the maximum, and 42.67% in the NIR (Near Infrared) channel.

• Journal of Life Science
• /
• v.19 no.10
• /
• pp.1360-1367
• /
• 2009

Liver fibrosis is a process of healing and scarring in response to chronic liver injury. Following injury, an acute inflammation response takes place resulting in moderate cell necrosis and extracellular matrix damage. To develop a novel therapeutic approach in hepatic fibrogenesis, we examined the simultaneous suppression of the transcription factors NF-$\kappa$B and Sp1, which regulate acute inflammation and continuous deposition of extracellular matrix in liver fibrosis. We employed chimeric decoy oligodeoxynucleotide containing the consensus sequences of both NF-$\kappa$B and Sp1 binding sites, to suppress these transcription factors simultaneously. Treatment of chimeric decoy oligodeoxynucleotide reduced the activity of hepatic stellate cells in vitro, and decreased the expression of fibrotic and proinflammatory gene responses in a mouse model of liver fibrosis. These results suggest that chimeric decoy oligodeoxynucleotide strategy can be a potential therapeutic application to prevent liver fibrosis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.2
• /
• pp.83-94
• /
• 2019

Recently, the importance of Total Life Cycle System Management (TLCSM) and LIFE-CYCLE COSTS management is increasing in the development of weapon systems. In cost management, cost forecasting is important from the initial development stage, but it is difficult to predict the total life cycle cost at the development stage. In this study, we propose efficient management cost calculation and management at the development stage of the weapon system by comparison analysis between the PRICE-HL model and NemoSIM to calculate the maintenance cost under the CAIV concept. Based on the study results, further in-depth analyzes of the PRICE-HL model and NemoSIM input values / results are performed. In addition, we provide a more accurate method of calculating the cost of maintaining and operating the weapon system and a plan to utilize the result of NemoSIM in the ILS element development.

• Journal of the Korean Society of Radiology
• /
• v.81 no.3
• /
• pp.632-643
• /
• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.201-220
• /
• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.