• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.591-601
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• 2018

This paper discusses methods to effectively operates and evaluates an infrastructure system for lane-level positioning based on satellite navigation. The lane-level positioning infrastructure provides correction information on range measurements with integrity information on the correction to a user with a single frequency (cheap) satellite navigation receiver in order to perform lane-level positioning and integrity monitoring on the position estimate. The architecture and configuration of the lane-level positioning system are described from the systematic level in order to provide a comprehensive insight of the system. The operation/evaluation system for the integrated infrastructure is then presented. The evaluation results of the real implemented system are provided. Based on the results, we discuss requirements to increase the system stability from the operation perspective.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.1-18
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• 2019

A short-turn bus is a bus that is operated within a subsection of an existing bus line. Previous studies regarding short-turn buses decided optimal turn-back points for a single bus line rather than a bus network. Also, in-vehicle crowding which has a significant impact on transit convenience was rarely considered. Therefore, this study aimed to develop a methodology to set priorities for the introduction of short-turn buses of bus lines and sections, considering crowding. To achieve this objective, we calculated occupancies and crowding alleviation benefits of existing bus lines overlapping a new short-turn route based on transit card data, before and after the introduction of short-turn strategy. Also, operator and social costs caused by the introduction of short-turn buses were calculated. Those procedures were iterated over bus lines and sections to operate a short-turn service, and a section whose benefit-to-cost ratio (B/C) is the largest in a line was selected to operate a short-turn service in the line. After, priorities of bus lines to introduce short-turn services could be determined based on B/C values, and the optimum total fleet size could be determined when a short-turn strategy is applied in multiple lines.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.48-57
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• 2019

The purpose of this study is to understand the intention of the inquirer from the single text type question in Goal-oriented dialogue. Goal-Oriented Dialogue system means a dialogue system that satisfies the user's specific needs via text or voice. The intention analysis process is a step of analysing the user's intention of inquiry prior to the answer generation, and has a great influence on the performance of the entire Goal-Oriented Dialogue system. The proposed model was used for a daily chemical products domain and Korean text data related to the domain was used. The analysis is divided into a speech-act which means independent on a specific field concept-sequence and which means depend on a specific field. We propose a classification method using the word embedding model and the CNN as a method for analyzing speech-act and concept-sequence. The semantic information of the word is abstracted through the word embedding model, and concept-sequence and speech-act classification are performed through the CNN based on the semantic information of the abstract word.

• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• Tunnel and Underground Space
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• v.31 no.4
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• pp.270-288
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• 2021

We proposed a numerical method to simulate the hydro-mechanical behavior of rock fracture using a grain-based distinct element model (GBDEM) in the paper. As a part of DECOVALEX-2023 Task G, we verified the method via benchmarks with analytical solutions. DECOVALEX-2023 Task G aims to develop a numerical method to estimate the coupled thermo-hydro-mechanical processes within the crystalline rock fracture network. We represented the rock sample as a group of tetrahedral grains and calculated the interaction of the grains and their interfaces using 3DEC. The micro-parameters of the grains and interfaces were determined by a new methodology based on an equivalent continuum approach. In benchmark modeling, a single fracture embedded in the rock was examined for the effects of fracture inclination and roughness, the boundary stress condition and the applied pressure. The simulation results showed that the developed numerical model reasonably reproduced the fracture slip induced by boundary stress condition, the fracture opening induced by fluid injection, the stress distribution variation with fracture inclination, and the fracture roughness effect. In addition, the fracture displacements associated with the opening and slip showed good agreement with the analytical solutions. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study experiments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.127-134
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• 2022

In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior were quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.67-75
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• 2020

Metonymy can be said to be the only language's meaning shifting technique that exists in the domain of a single human thought in order to obtain a transparent cognitive effect. The purpose of this study was to analyze the 'descriptive metonymy' of the advertising content language constructed by the cognitive principle and to find a way to use it in media language education for social and cultural interests and reflection of college students. The metonymy used in advertising media contrasts with the difficulty of the metaphorical interpretation of "opaque and distant" reasoning. Storyboards, mostly focused on human emotions and behaviors, used metonymy's 'transparent and easy meaning shifting technique'. I have found that I can expect the efficiency of media language education that contains the interest and sociocultural interest, self-reflection, and future imagination of college students. Now, there is less need to perform cognitive reasoning for advertisements with ambiguous metaphor techniques. Lastly, in order to produce successful advertising content, we expect to use the language technique of 'narrative metonymy' with warm feelings of humans, and acknowledge the lack of quantitative research and leave it as a task for the next research.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.