• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.516-521
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• 2022

In this paper, a compact 4-bit chipless RFID(radio frequency identification) tag using a modified ELC(electric field-coupled inductive-capacitive) resonator and multiple slot resonators is proposed. The modified ELC resonator uses an interdigital-capacitor structure in the conventional ELC resonator to lower the resonance peak frequency of the RCS. The multiple slot resonators are designed by etching three slots with different lengths into an inverted U-shaped conductor. The resonant peak frequency of the RCS for the modified ELC resonator is 3.216 GHz, whereas those of the multiple slot resonators are set at 4.122 GHz, 4.64 GHz, and 5.304 GHz, respectively. The proposed compact four-bit tag is fabricated on an RF-301 substrate with dimensions of 50 mm×20 mm and a thickness of 0.8 mm. Experiment results show that the resonant peak frequencies of the fabricated four-bit chipless RFID tag are 3.285 GHz, 4.09 GHz, 4.63 GHz, and 5.31 GHz, respectively, which is similar to the simulation results with errors in the range between 0.78% and 2.16%.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.125-134
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• 2023

The purpose of this paper is to propose a method to improve the performance of the previous research is characterized by recognizing facial gestures from the 3D coordinates of seven landmarks selected from the MediaPipe Face Mesh model, generating corresponding user events, and executing corresponding commands. The proposed method applied adaptive moving average processing to the cursor positions in the process to stabilize the cursor by alleviating microtremor, and improved performance by blocking temporary opening/closing discrepancies between both eyes when opening and closing both eyes simultaneously. As a result of the usability evaluation of the proposed facial gesture interface, it was confirmed that the average recognition rate of facial gestures was increased to 98.7% compared to 95.8% in the previous research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.659-665
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• 2023

The exciter of a ship generator adjusts the magnetic flux through excitation current control to maintain the output terminal voltage constant. The voltage controller inside the exciter typically uses a proportional integral control method. however, the response characteristics determined by the gain and time constant produce unwanted output owing to an inappropriate setting value that can reduce the quality and stability of power within the ship. In this study, a neural network circuit is learned using stable input/output data that can be obtained through the AC4A type exciter model provided by IEEE, and the simulation is performed by replacing the existing proportional integral control type voltage controller with the learned neural network circuit controller. Consequently, overshooting was improved by up to 9.63% compared with that of the previous model, and excellence in stable response characteristics was confirmed.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.310-316
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• 2023

This study aims to predict uncertain future scenarios that may unfold in South Korea in the near future, utilizing the theory of extreme events(X-events). A group of 32 experts, consisting of government emergency planning officers, was selected as the focus group to achieve this objective. Using the Focus Group Interview (FGI) technique, opinions were gathered from this focus group regarding potential X-events that may occur within the advanced science and technology domains over the next 10 years. The analysis of these opinions revealed that government emergency planning officers regarded the "Obsolescence of current technology and systems," particularly in the context of cyber network paralysis as the most plausible X-event within science and technology. They also put forth challenging and intricate opinions, including the emergence of new weapon systems and ethical concerns associated with artificial intelligence (AI). Given that X-events are more likely to emerge in unanticipated areas rather than those that are widely predicted, the results obtained from this study carry significant importance. However, it's important to note that this study is grounded in a limited group of experts, highlighting the necessity for subsequent research involving a more extensive group of experts. This research seeks to stimulate studies on extreme events at a national level and contribute to the preparation for future X-event predictions and strategies for addressing them.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.357-362
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• 2023

Bridges are important transportation infrastructure, but they are subject to damage and cracking due to various environmental factors and constant traffic loads, which accelerate their aging. With many bridges now older than their original construction, there is a need for systems to ensure safety and diagnose deterioration. Bridges are already utilizing structural health monitoring (SHM) technology to monitor the condition of bridges in real time or periodically. Along with this technology, the development of intelligent bridge monitoring technology utilizing artificial intelligence and Internet of Things technology is underway. In this paper, we study an edge system technique for predicting bridge safety using fast Fourier transform and dimensionality reduction algorithm for maintenance of aging bridges. In particular, unlike previous studies, we investigate whether it is possible to form a dataset using sensor data collected from actual bridges and check the safety of bridges.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.403-416
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• 2024

Malware is being commercialized and sold on the black market, primarily driven by financial incentives. With the increasing demand driven by these sales, the scope of attacks via malware has expanded. In response, there has been a surge in research efforts leveraging artificial intelligence for detection and classification. However, adversaries are integrating various anti-analysis techniques into their malware to thwart analytical efforts. In this study, we introduce the "Malware Analysis with Dynamic Extraction (MADE)" framework, a hybrid binary analysis tool devised to procure datasets from advanced malware incorporating Anti-Analysis techniques. The MADE framework has the proficiency to autonomously execute dynamic analysis on binaries, encompassing those laden with Anti-VM and Anti-Debugging defenses. Experimental results substantiate that the MADE framework can effectively circumvent over 90% of diverse malware implementations using Anti-Analysis techniques and can adeptly extract relevant datasets.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.65-88
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• 2024

This pilot study explores a method of extracting metadata values and descriptions from records using named entity recognition (NER), a technique in natural language processing (NLP), a subfield of artificial intelligence. The study focuses on handwritten records from the Guro Industrial Complex, produced during the 1960s and 1970s, comprising approximately 1,200 pages and 80,000 words. After the preprocessing process of the records, which included digitization, the study employed a publicly available language API based on Google's Bidirectional Encoder Representations from Transformers (BERT) language model to recognize entity names within the text. As a result, 173 names of people and 314 of organizations and institutions were extracted from the Guro Industrial Complex's past records. These extracted entities are expected to serve as direct search terms for accessing the contents of the records. Furthermore, the study identified challenges that arose when applying the theoretical methodology of NLP to real-world records consisting of semistructured text. It also presents potential solutions and implications to consider when addressing these issues.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.113-122
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• 2024

Recently, cutting-edge ICT technologies such as artificial intelligence, blockchain, edge computing, and the Internet of Things have been applied in various fields to create new services and a new digital era. Along with these technological developments, various policies are being implemented in Korea to transform the country from a "Smart City" to a "Platform City". We can create new services and values by linking with the Smart City Integrated Platform and Smart Housing Platform. This paper defines a linkage scenario between a Smart Housing Platform and the Smart 119 Emergency Dispatch Support Service, one of the Smart City Safety Nets. We propose a data transmission protocol and a metadata schema for data exchange between the Smart Housing Platform and the Smart City Integrated Platform to provide the Smart 119 Emergency Dispatch Support Service.