• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.6
• /
• pp.543-551
• /
• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.613-618
• /
• 2023

The advancement of Information and Communication Technology (ICT) is accelerating innovation across society, and the defense sector is no exception as it adopts technologies aligned with the Fourth Industrial Revolution. In particular, the Army is making efforts to establish an advanced Army TIGER 4.0 system, aiming to create highly intelligent and interconnected mobile units. To achieve this, the Army is integrating cutting-edge scientific and technological advancements from the Fourth Industrial Revolution to enhance mobility, networking, and intelligence. However, the existing addressing system, IPv4, has limitations in meeting the exponentially increasing demands for network IP addresses. Consequently, the military considers IPv6 address allocation as an essential process to ensure efficient network management and address space provisioning. This study proposes an approach for IPv6 address allocation for the future military, considering the Army TIGER system. The proposal outlines how the application networks of the Army can be differentiated, and IP addresses can be allocated to future unit structures of the Army, Navy, and Air Force, from the Ministry of National Defense and the Joint Chiefs of Staff. Through this approach, the Army's advanced ground combat system, Army TIGER 4.0, is expected to operate more efficiently in network environments, enhancing overall information exchange and mobility for the future military.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.34 no.3
• /
• pp.79-85
• /
• 2024

We investigated the crystal structural property and chemical bonding nature of cellulose nanocrystal extracted directly from cotton cellulose using high-pressure homogenizer. The nanowire-like cellulose nanocrystals were randomly distributed in the form of a dense mesh. Based on calculating the interplanar distance of the Bragg-diffracted crystal plane observed through X-ray diffraction (XRD) analysis, it was found that the cellulose nanocrystals formed by high-pressure homogenizer had a monoclinc crystal structure, corresponding to the cellulose Iβ sub-polymorph. Solid-state nuclear magnetic resonance (NMR) analysis for the quantitatively evaluation of the amorphous region in cellulose nanocrystals revealed that the crystallinity index of cellulose nanocrystals was calculated to be 53.06 %. The O/C ratio of the surface of cellulose nanocrystal was estimated to be 0.82. Further analysis showed that chemical bonds of C-C bond or C-H bond, C-O bond, O-C-O bond or C=O bond, and O-C=O bond were the main chemical bonding states of the cellulose nanocrystal surface.

• Convergence Security Journal
• /
• v.23 no.4
• /
• pp.109-119
• /
• 2023

C4I system is an integrated battlefield information system that automates the five elements of command, control, communications, computers, and information to efficiently manage the battlefield. C4I systems play an important role in collecting and analyzing enemy positions, situations, and operational results to ensure that all services have the same picture in real time and optimize command decisions and mission orders. However, the current C4I has limitations whenever a new weapon system is introduced, as it only provides battlefield visualization in a single area focusing on the battlefield situation for each military service. In a future battlefield that expands not only to land, sea, and air domains but also to cyber and space domains, improved command and control decisions will be possible if organic data from various weapon systems is gathered to quickly visualize the battlefield situation desired by the user. In this study, the visualization technology applicable to the future C4I system is divided into map area, situation map area, and display area. The technological implementation of this future C4I system is based on various data and communication means such as 5G networks, and is expected to enable hyper-connected battlefield visualization that utilizes a variety of high-quality information to enable realistic and efficient battlefield situation awareness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.3
• /
• pp.173-186
• /
• 2024

This paper analyzes a case of successful faculty entrepreneurship through a coupled process of open innovation in a university context, using the core competency theory perspective. Initially, the current state of faculty entrepreneurship is examined, and the effects of interdisciplinary coupled processes of open innovation are explored, focusing on the case of 'Omotion Inc.,' a startup utilizing generative AI technology for hyper-realistic 3D virtual human experiences. The research methodology involves in-depth interviews with Omotion Inc.'s co-founders, technology commercialization professionals, and experts in the field, followed by analysis based on foundational theories. Applying the core competency theory, this paper scrutinizes the process of integrating diverse expertise and technologies from various academic disciplines. The analysis goes beyond the limitations of faculty entrepreneurship confined to a single technology-centric research domain. Instead, it explores the possibilities of enhancement and value creation through coupled processes, providing practical implications for the university entrepreneurial ecosystem. The aim is to extend the traditional roles of education and research within the university, presenting a role in economic value creation beyond the boundaries of conventional faculty entrepreneurship. Through the collaboration of two faculty members, this study showcases the creation of novel technology and business models. It establishes that successful coupled processes of open innovation in faculty entrepreneurship, from a core competency theory perspective, require the entrepreneurial firm to possess (1) entrepreneurial capabilities, (2) technological capabilities, and (3) networking capabilities. The implications of this research highlight the positive impact of coupled processes of open innovation in faculty entrepreneurship, as evidenced by the Omotion Inc. case, offering guidance on entrepreneurial directions for university members preparing for entrepreneurship.

• Journal of Internet Computing and Services
• /
• v.25 no.5
• /
• pp.95-105
• /
• 2024

With the recent adoption of AI by various clothing shopping platforms and related industries to meet consumer needs and enhance purchasing power, the necessity for accurate classification of clothing categories and colors has surged. This paper aims to address this issue by developing a deep learning model that classifies various clothing items and their colors within a single image using buyer review images. After directly crawling buyer review image data and performing various preprocessing steps such as data augmentation, we utilized the YOLOv10 model to detect clothing objects and classify them into categories. Subsequently, to improve color extraction, we implemented a cropping method to isolate clothing regions in the images and calculated the similarity with a color chart to extract the most similar color names. Our experimental results show that our approach is effective, with performance increasing with model size and augmentation scale. The employed model showed stable performance in both clothing category and color extraction, proving its reliability. The proposed system not only enhances customer satisfaction and purchasing power by accurately classifying clothing categories and colors based on user review images but also lays the foundation for further research in automated fashion analysis. Moreover, it possesses the scalability to be utilized in various fields of the related industry, such as fashion trend analysis, inventory management, and marketing strategy development.

• Industry Promotion Research
• /
• v.9 no.3
• /
• pp.163-174
• /
• 2024

MyData is a new entity that strengthens the rights of information subjects through the 'right to data portability' and utilizes data to enable hyper-personalized services using personal information. Korea's MyData system is recognized globally as an outstanding system in that it is creating a new MyData industry by granting the right to information self-determination through the 'right to request data transmission'. Now in its third year, this study evaluates Korea's MyData system from a consumer behavior perspective and identifies issues for improvement. To this end, this study reviewed previous research on the relationship between regulatory policy and consumer behavior to determine the applicability of a consumer behavior perspective in institutional evaluation. In addition, in a study on consumer behavior related to MyData, variables that affect the use of MyData were investigated and evaluation items from a consumer behavior perspective were derived. As a result of evaluating Korea's MyData system from a consumer behavior perspective, it was found that the factors considered important by consumers were appropriately reflected in the system. However, in cases where there are dual values of ease of use and personal information protection, regulatory aspects tend to take priority. Therefore, in order to revitalize the MyData industry, it is essential to implement market-friendly system improvements without compromising consumer rights. This study is differentiated from existing studies in that it attempted to derive a plan for system improvement by combining empirical consumer behavior research and regulatory policy research.

• Journal of Korea Water Resources Association
• /
• v.57 no.9
• /
• pp.585-598
• /
• 2024

To mitigate natural disasters and efficiently manage water resources, it is essential to enhance hydrologic prediction while reducing model structural uncertainties. This study analyzed the impact of lumped and semi-distributed GR4J model structures on simulation performance and evaluated uncertainties with and without data assimilation techniques. The Ensemble Kalman Filter (EnKF) and Particle Filter (PF) methods were applied to the Namgang Dam basin. Simulation results showed that the Kling-Gupta efficiency (KGE) index was 0.749 for the lumped model and 0.831 for the semi-distributed model, indicating improved performance in semi-distributed modeling by 11.0%. Additionally, the impact of uncertainties in meteorological forcings (precipitation and potential evapotranspiration) on data assimilation performance was analyzed. Optimal uncertainty conditions varied by data assimilation method for the lumped model and by sub-basin for the semi-distributed model. Moreover, reducing the calibration period length during data assimilation led to decreased simulation performance. Overall, the semi-distributed model showed improved flood simulation performance when combined with data assimilation compared to the lumped model. Selecting appropriate hyper-parameters and calibration periods according to the model structure was crucial for achieving optimal performance.

• Korean Journal of Psychosomatic Medicine
• /
• v.19 no.2
• /
• pp.74-82
• /
• 2011

Objective:The aim of this study was to evaluate the relationship between sustained attention deficits in Attention-Deficit/Hyperactivity Disorder(ADHD) children and short-term Heart Rate Variability(HRV) parameters. In addition, we evaluate the relationship between The ADHD rating scale(ARS), the computerized ADHD diagnostic system(ADS) and Parenting stress index- short form(PSI-SF). Methods:This study was performed in the department of children and Adolescent psychiatry, Korea university Guro hospital from august 2008 to January 2009. We evaluated HRV parameters by short-term recordings of 5 minutes. K-ARS and ADS are used for screening and identifying ADHD children. Intelligence was measured using Korean educational Developmental Institute-wechsler Intelligence Scale for Children. The caregivers Complete Parenting Stress Index scale for evaluation parent stress. Results:The low frequency(LF) was significantly correlated with response variability of ADS. However, the other variables of ARS and ADS were not significantly correlated with LF. Hyperactivity subscale of ARS was significantly correlated with parental distress subscale and difficult child subscale of PSI-SF and inattention subscale of ARS was also significantly correlated with dysfunctional interaction and difficult child subscale of PSI-SF. Conclusion:The LF, 0.10-Hz component of HRV is known to measure effort allocation. This study shows that the LF component of HRV is significantly correlated with the response variability of ADS. This means that more severe symptoms of ADHD were correlated with the increase in the LF that means decreased effort allocation. These results also support the clinical usability of HRV in the assessment of ADHD. Furthermore, PSI-SF is correlated with hyperactivity and inattention variables of ARS.