• 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.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.136-141
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• 2024

In this paper, a broadband inset-fed microstrip patch antenna operating at 10.525 GHz band was proposed. The proposed broadband inset-fed microstrip patch antenna consists of three narrow rectangular patches. At the center of the center patch, two symmetrical side patches were connected by a strip conductor and were arranged with their centers shifted in a perpendicular direction with respect to the center patch. For performance comparison, a conventional inset-fed square microstrip patch antenna was designed. Experiment results show that the frequency band of the measured input reflection coefficient with a voltage standing wave ratio less than 2 for the broadband inset-fed microstrip patch antenna was 10.036-11.051 GHz (9.63%), whereas that for the conventional inset-fed rectangular microstrip patch antenna was 10.306-10.772 GHz (4.42%). Therefore, the input reflection coefficient frequency bandwidth of the fabricated broadband inset-fed microstrip patch antenna was increased by 2.18 times, compared to the conventional inset-fed square microstrip patch antenna.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.815-820
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• 2023

In this paper, the design method of a miniaturized chipless RFID tag using a modified bent H-shaped slot was proposed. The proposed modified bent H-shaped slot was appended on the rectangular conductor plate printed on one side of a 20 mm × 50 mm FR4 substrate with a thickness of 0.8 mm. The resonant dip frequency of the bistatic RCS for the proposed modified bent H-shaped slot was compared with the cases when the H-shaped, U-shaped slot, and bent H-shaped slots were added, respectively, on the conductor plate. The simulated resonant dip frequencies for H-shaped, U-shaped, and bent H-shaped slots were 5.907 GHz, 4.918 GHz, and 4.364 GHz, respectively. When the proposed modified bent H-shaped slot was added, the resonant dip frequency was decreased to 3.741 GHz, and, therefore, the slot length was reduced by 36.7% compared to the H-shaped slot case. Experiment results show that the resonant dip frequency of the fabricated modified bent H-shaped slot was 3.9 GHz.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.625-632
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• 2024

This paper analyzed the changes in the curriculum of computer and software-related majors that educate the core ICT technologies needed for the 4th Industrial Revolution, before and after the COVID-19 pandemic. According to the standard classification of university education units, 172 majors classified into Applied Software Engineering, Computer Science·Computer Engineering, and Artificial Intelligence Engineering were targeted, and the curricula of 2023 and 2019 were compared and analyzed. As a result of the analysis, the introduction of the related curriculum for each curriculum group increased by about 2.6%p before and after the COVID-19 pandemic (2023 84.2%, 2019 81.6%). and the 4th Industrial Revolution response index increased by 9.5 points (37.0 in 2023, 27.5 in 2019)

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.356-361
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• 2024

This paper deals with the design and fabrication of a coplanar waveguide (CPW)-fed super-wideband semicircular-disk-shaped dipole antenna operating in a frequency band of 2.4 GHz or higher. To feed the antenna, a CPW feed line was appended to the center of the lower arm of the semicircular-disk-shaped dipole antenna. For miniaturization, square patches were added to the ends of the two arms of the semicircular-disk-shaped dipole, whereas the slot width of the CPW feed line at the center of the dipole antenna was increased to improve impedance matching in the 5.4-6.3 GHz band. The simulated frequency band of the proposed antenna for a voltage standing wave ratio (VSWR) less than 2 was 2.369-30 GHz(170.7%), whereas the fabricated antenna was maintained VSWR less than 2 in the frequency range of 2.378-20 GHz when measured using a network analyzer operating up to 20 GHz so it can be applied as a super-wideband antenna for next-generation mobile communications.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.93-98
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• 2024

With the rapid advance in AI (artificial intelligence) and high-performance computing technologies, deep learning is being used in various fields. Deep learning proceeds training by randomly reading a large amount of data and repeats this process. A large number of files are randomly repeatedly referenced during deep learning, which shows different access characteristics from traditional workloads with temporal locality. In order to cope with the difficulty in caching caused by deep learning, we propose a new sampling method that aims at reducing the randomness of dataset reading and adaptively operating on existing buffer cache algorithms. We show that the proposed policy reduces the miss rate of the buffer cache by 16% on average and up to 33% compared to the existing method, and improves the execution time by up to 24%.