• Smart Media Journal
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• v.11 no.5
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• pp.56-62
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• 2022

Water reservoir is defined as a storage space to hold and supply filtered water and it's significantly important to manage water level in the water reservoir so as to stabilize water supply by controlling water supply depending on demand. Liquid level sensors have been installed in the water reservoir and the pumps in the booster station facilitated management for optimum water level in the water reservoir. But the incident responses including sensor malfunction and communication breakdown actually count on manager's inspection, which involves risk of accidents. To stabilize draining facility management, this study has come up with AI model that predicts changes in the water level in the water reservoir. Going through simulation in the case of missing data in the water level to verify stability in relation to the field application of the prediction model for water level changes in the reservoir, the comparison of actual change value and predicted value allows to test utility of the model.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.595-602
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• 2023

The EAST ICRF system operating space has been extended in power and phase control with a low-level RF system for the new double-strap antenna. Then the multi-step power and periodic phase scanning experiment were conducted in L-mode plasma, respectively. In the power scanning experiment, the stored energy, radiation power, plasma impedance and the antenna's temperature all have positive responses during the short ramp-ups of P_L;ICRF. The core ion temperature increased from 1 keV to 1.5 keV and the core heating area expanded from |Z| ≤ 5 cm to |Z| ≤ 10 cm during the injection of ICRF waves. In the phasing scanning experiment, in addition to the same conclusions as the previous relatively phasing scanning experiment, the superposition effect of the fluctuation of stored energy, radiation power and neutron yield caused by phasing change with dual antenna, resulting in the amplitude and phase shift, was also observed. The active control of RF output facilitates the precise control of plasma profiles and greatly benefits future experimental exploration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1898-1907
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• 2022

Every year, many shipping companies provide seaman safety education programs periodically to reduce ocean-traffic accidents. However, undertaking the regular safety training for seaman has been difficult because of litimation of space and time. Recently, VR technolgy is received attentions to overcome previous problems. It can provide users educational interactions between a user and virtual environment and fulfill sustainable teaching. In this paper, VR-based safety education content for sailors has been developed, and it includes four programs. Also, survey was conducted with four questionnaires such as immersiveness, easy to experience, satisfaction of education contents, comparative evaluation between traditional education program and VR education contents. As the result, immersiveness questionnaire could be gain 53.83% positive assessment, and easy to experience could be gain 65.38% positive assessment, and satisfaction could be gain 69.23% positive assessment. Lastly, comparative evaluation between traditional education program and VR education contents could be gain about 46% positive and 34% neutral assessments.

• Journal of the Korean institute of surface engineering
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• v.57 no.3
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• pp.214-220
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• 2024

In this study, three-dimensional (3D) networks structure using single-walled carbon nanotubes (SWCNTs) for Si-graphite composite electrode was developed and studied about effects on the electrochemical performances. To investigate the effect of SWCNTs on forming a conductive 3D network structure electrode, zero-dimensional (0D) carbon black and different SWCNTs composition electrode were compared. It was found that SWCNTs formed a conductive network between nano-Si and graphite particles over the entire area without aggregation. The formation of 3D network structure enabled to effective access for lithium ions leading to improve the c-rate performance, and provided cycle stability by alleviating the Si volume expansion from flexibility and buffer space. The results of this study are expected to be applicable to the electrode design for high-capacity lithium-ion batteries.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.105-112
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• 2024

In contemporary society, the expression of emotions and self-reflection are considered pivotal factors with a positive impact on stress management and mental well-being, thereby highlighting the significance of journaling. However, traditional journaling methods have posed challenges for many individuals due to constraints in terms of time and space. Recent rapid advancements in chatbot and emotion analysis technologies have garnered significant attention as essential tools to address these issues. This paper introduces an artificial intelligence chatbot that integrates the GPT-3 model and emotion analysis technology, detailing the development process of a system that automatically generates journals based on users' chat data. Through this system, users can engage in journaling more conveniently and efficiently, fostering a deeper understanding of their emotions and promoting positive emotional experiences.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.146-154
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• 2023

In this study, the peak wave period T_p and mean wave period T₀₂ and T_{m-1, 0}, which are major parameters for classifying ocean characteristics, were calculated using water surface elevation data observed from the second west coast oceanographic and meteorological observation tower. In addition, the ratio of abnormal data, correlation analysis, and optimal probability density function were estimated. In the case of T_p among the calculated representative periods, the proportion of abnormal data was 5.73% and 0.67% at each point, and T₀₂ was 4.35% and 0.01%. T_{m-1, 0} was found to be 2.82% and 0.03%. Meanwhile, as a result of analyzing the relationship between T₀₂ and T_p, the relationship was calculated to be 0.53 and 0.63 for each point. The relationship between T_{m-1, 0} and T_p was 1.15 and 1.32, respectively, and T₀₂, T_{m-1, 0} was 1.18 and 1.22. As a result of estimating the optimal probability density function of the calculated representative period, T_p followed the 'Log-normal' and 'Normal' distributions at each point, and T₀₂ was 'Gamma', 'Normal' distribution and T_{m-1, 0} showed that 'Log-normal' and 'Normal' distribution were dominant, respectively. It is decided that these results can be used as basic data for wave analysis conducted on the west coast.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.194-204
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• 2010

A space, real or virtual, comprises regions as its parts and physical objects residing in them. A coherent and sophisticated representaion scheme for their spatial relations premises the precision and plausibility in its associated agents' inferencing on the spatial relations and the development of events occurring in such a space. The existing spatial models are not suitable for a comprehensive representation of the general spatial relations in that they have limited expressive powers based on the dichotomy between the large and small scales, or support only a small set of topological relations. The representaion model we propose has the following distinctive chracteristics: firstly, our model provides a comprehensive representation scheme to accommodate large and small scale spaces in an integrated fashion; secondly, our model greatly elaborated the spatial relations among the small-scale objects based on their contact relations and the compositional relations among their respective components objects beyond the basic topological relations like disjoint and touch; thirdly, our model further diversifies the types of supported relations by adding the container property besides the soildness together with considering the gravity direction. The resulting integrated spatial knowledge representation scheme considering the gravity allows the diverse spatial relations in the real world to be simulated in a precise manner in relation to the associated spatial events and provides an expression measure for the agents in such a cyber-world to capture the spatial knowledge to be used for recognizing the situations in the spatial aspects.