• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.6
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• pp.481-488
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• 2021

The satellite telecommand processing system is the only way to provide telecommands for status monitoring, control, and mission execution. Domestic satellites can be divided into science, technology, and multi-purpose satellites, and geostationary satellites. These Satellites uses CCSDS standard protocol to communicate with ground stations. However, existing domestic satellites use only software to decode telecommands which increases cost of software development and verification of the developed software. Performance of software only approach is relatively low compared to hardware. In this paper, we present ASIC processing system specifically designed to decode telecommands. The system consists of a telecommand RAM, a protocol RAM/ROM, an ASIC, an interface unit of FPGA, and a relay block. The system handles general commands and pulse commands that are used in satellites. We established a ground station equipment and test environment to verify the system functionality, The result shows that our system reduces the development cost by 1/5 and improves the performance by 10⁵ times compared to the previous systems that decode telecommands only by software.

• The Research Journal of the Costume Culture
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• v.27 no.5
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• pp.449-460
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• 2019

Today, the fashion business environment of the 4.0 generation is changing based on fashion technology combined with advanced digital technologies such as AI (Artificial Intelligence), big data and IoT (Internet of Things). "Digital Transformation" means a fundamental change and innovation in a digital paradigm including corporate strategy, organization, communication, and business model, based on the utilization of digital technology. Thus, this study examines digital transformation strategies through the fashion brand Burberry. The study contents are as follows. First, it examines the theoretical concept of digital transformation and its utilization status. Second, it analyzes the characteristics of Burberry's digital transformation based on its strategies. For the research methodology, a literature review was performed on books and papers, aligning with case studies through websites, social media, and news articles. The result showed that first, Burberry has reset their main target to Millennials who actively use mobile and social media, and continues to communicate with them by utilizing digital strategy in the entire management. Second, Burberry is quickly delivering consistent brand identity to consumers by internally creating and providing social media-friendly content. Third, they have started real-time product sales and services by using IT to enhance access to brands and to lead consumers towards more active participation. In this study, Burberry's case shows that digital transformation can contribute to increased brand value and sales, keeping up with the changes in the digital paradigm. Therefore, the study suggests that digital transformation will serve as an important business strategy for fashion brands in the future.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.43-50
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• 2024

This manuscript proposes an artificial intelligence-based(AI) energy platform system that efficiently use existing energy than creating new energy than creating new energy sources. To this end, it collects public information data portal and statistics data portal and data emissions, including energy usage and greenhouse gas emissions, including energy consumption and greenhouse gas emissions.In addition, it provides strong security and personal information protection functions to overcome the limit of existing energy platform. Through the built energy platform, improving power supply and user convenience of users and users to contribute to global warming issues.In this paper, the contents to implement the contents of the system, and improvement direction from the future completion and improvement direction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.224-231
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• 2020

An optical tracking plays an important role for measurement operation, as it is responsible for low altitude measurements that are difficult to obtain with radar systems. Since the existing optical tracking systems have not been developed in the proving ground itself so far, it is difficult to modify them to fit the environment of the proving ground. Also, they are designed as a vehicle-mounted type, so there is a limitation in selecting an optimal site. The in-house developed small optical tracking system is designed with a simple configuration to overcome these shortcomings and makes it possible for operators to operate the system at any place in the proving ground. In addition, there has been a need of developing small optical trackers by ourselves to be prepared for future research so that artificial intelligence (AI) can be applied to the optical tracking systems. In this paper, we described the design concept of the small optical tracker, the configuration of the components to implement the basic tracking function, and showed the results of the simulation to set the configuration of the equipment according to the characteristics of the flight targets.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Journal of KIISE
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• v.43 no.2
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• pp.222-228
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• 2016

Uniform navigation of NPCs in computer games is an important factor that can decrease the interest of game players. This problem is particularly noticeable in pathfinding when using a waypoint graph because the NPCs navigate using only predefined locations. In this paper we propose a method that enables adaptive navigations of NPCs by observing player movements. The proposed method involves modification of waypoints dynamically by observing the player's point designation and use of the modified waypoints for NPC's pathfinding. Also, we propose an algorithm to find the NPC-specific path by learning the landform preferences of players. We simulate the implemented algorithm in an RPG game made with Unity 4.0 and confirm that NPC navigations had more variety and improved according to player navigations.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.533-540
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• 2021

To evaluate the performance of a system, one-dimensional 3-neighborhood cellular automata(CA) based pseudo-random generators are widely used in many fields. Although two-dimensional CA and one-dimensional 5-neighborhood CA have been applied for more effective key sequence generation, designing symmetric one-dimensional 5-neighborhood CA corresponding to a given primitive polynomial is a very challenging problem. To solve this problem, studies on one-dimensional 5-neighborhood CA synthesis, such as synthesis method using recurrence relation of characteristic polynomials and synthesis method using Krylov matrix, were conducted. However, there was still a problem with solving nonlinear equations. To solve this problem, a symmetric one-dimensional 5-neighborhood CA synthesis method using a transition matrix of 90/150 CA and a block matrix has recently been proposed. In this paper, we detail the theoretical process of the proposed algorithm and use it to obtain symmetric one-dimensional 5-neighborhood CA corresponding to high-order primitive polynomials.