• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.111-117
• /
• 2023

There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.

• Industry Promotion Research
• /
• v.8 no.4
• /
• pp.257-266
• /
• 2023

The research focuses on the provincial universities in the western region of China and investigates the research level of 12 provincial universities from 2017 to 2021, considering both static efficiency and dynamic efficiency. The static efficiency is examined using Data Envelopment Analysis (DEA), while the dynamic efficiency is analyzed using the Malmquist model. The analysis results are as follows: the scientific research efficiency of universities in the 12 western provinces is generally not high. Against the background of the "Double First-Class" construction, the overall efficiency of scientific research in universities is showing an increasing trend. The main reason for the increase in scientific research efficiency is the increase in scale efficiency in recent years. The total factor productivity (TFP) of research activities is influenced by the technology progress index and exhibits a pattern of initial increase, followed by a decline, and then an increase again. Research conclusion: Western colleges and universities should reasonably allocate resources for scientific research activities, perfect scientific research mechanisms, improve management standards, promote scientific innovation and corresponding achievements, and ultimately raise the scientific and technological level in western China.

• Smart Media Journal
• /
• v.12 no.9
• /
• pp.60-71
• /
• 2023

A microservice architecture that accommodates the heterogeneity of various development environments and enables flexible maintenance can secure business agility to manage services in line with rapidly changing requirements. Due to the nature of MSA, where communication between microservices within a service is frequent, the boundary security that has been used in the past is not sufficient in terms of security, and a Zerotrust system is required. In addition, as the size of microservices increases, definition of access control policies according to the API format of each service is required, and difficulties in policy management increase, such as unnecessary governance overhead in the process of redistributing services. In this paper, we propose a microservice architecture that centrally manages policies by separating access control decision and enforcement with a general-purpose policy engine called OPA (Open Policy Agent) for collective and flexible policy management in Zerotrust security-applied environments.

• Smart Media Journal
• /
• v.12 no.9
• /
• pp.19-27
• /
• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.6
• /
• pp.237-244
• /
• 2022

In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.927-934
• /
• 2023

With the recent growth of the companion animal market, various social issues related to companion animals have also come to the forefront. Notable problems include incidents of dog bites, the challenge of managing abandoned companion animals, euthanasia, animal abuse, and more. As potential solutions, a variety of training programs such as companion animal-focused broadcasts and educational apps are being offered. However, these options might not be very effective for novice caretakers who are uncertain about what to prioritize in training. While training apps that are relatively easy to access have been widely distributed, apps that allow users to directly engage in training and learn through hands-on experience are still insufficient. In this paper, we propose a more efficient AR-based mobile app for companion animal training, utilizing the Unity engine. The results of usability evaluations indicated increased user engagement due to the inclusion of elements that were previously absent. Moreover, training immersion was enhanced, leading to improved learning outcomes. With further development and subsequent verification and production, we anticipate that this app could become an effective training tool for novice caretakers planning to adopt companion animals, as well as for experienced caretakers.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1229-1238
• /
• 2023

Digital twin is a technology that creates a virtual space that replicates the real world and manages the real world efficiently by integrating the real and virtual spaces. The digital twin concept for water facilities is to effectively manage water pipes in the real world by implementing them in a virtual space and augmenting them to the interior space of the building. In the proposed method, the Unity 3D game engine is used to implement the application of digital twin technology in the interior of a building. The AR Foundation toolkit based on ARCore is used as the augmented reality technology for our Digital Twin implementation. In digital twin applications, it is essential to match the real and virtual worlds. In the proposed method, 2D image markers are used to match the real and virtual worlds. The Unity shader program is also applied to make the augmented objects visually realistic. The implementation results show that the proposed method is simple but accurate in placing water pipes in real space, and visually effective in representing water pipes on the wall.

• Journal of Aerospace System Engineering
• /
• v.18 no.1
• /
• pp.29-36
• /
• 2024

Recently, the concept of Urban Air Mobility (UAM) has expanded to Advanced Air Mobility (AAM). A tilt rotor type of vertical take-off and landing aircraft has been actively studied and developed. A tilt-rotor aircraft can perform a transition flight between vertical and horizontal flights. A blade pitch angle control system can be used for flight stability during transition flight time. In addition, Individual Blade Control (IBC) can reduce noise and vibration generated in transition flight. This paper proposed Disturbance Observer Based Control (DOBC) and Time Delay Control (TDC) for individual blade control of an Electro-Mechanical Actuator (EMA) based blade pitch angle control system. To compare and analyze proposed controllers, numerical simulations were conducted with DOBC and TDC.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.2
• /
• pp.35-45
• /
• 2024

In this paper, we propose an intra prediction method for depth picture using CNN and Attention mechanism. The proposed method allows each pixel in a block to predict to select pixels among reference area. Spatial features in the vertical and horizontal directions for reference pixels are extracted from the top and left areas adjacent to the block, respectively, through a CNN layer. The two spatial features are merged into the feature direction and the spatial direction to predict features for the prediction block and reference pixels, respectively. the correlation between the prediction block and the reference pixel is predicted through attention mechanism. The predicted correlations are restored to the pixel domain through CNN layers to predict the pixels in the block. The average prediction error of intra prediction is reduced by 5.8% when the proposed method is added to VVC intra modes.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.2
• /
• pp.43-50
• /
• 2024

This paper evaluates the Korean sentiment analysis performance of large language models like GPT-3.5 and GPT-4 using a zero-shot approach facilitated by the ChatGPT API, comparing them to pre-trained Korean models such as KoBERT. Through experiments utilizing various Korean sentiment analysis datasets in fields like movies, gaming, and shopping, the efficiency of these models is validated. The results reveal that the LMKor-ELECTRA model displayed the highest performance based on F1-score, while GPT-4 particularly achieved high accuracy and F1-scores in movie and shopping datasets. This indicates that large language models can perform effectively in Korean sentiment analysis without prior training on specific datasets, suggesting their potential in zero-shot learning. However, relatively lower performance in some datasets highlights the limitations of the zero-shot based methodology. This study explores the feasibility of using large language models for Korean sentiment analysis, providing significant implications for future research in this area.