• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.139-161
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• 2024

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

• Current Optics and Photonics
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• v.8 no.5
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• pp.508-514
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• 2024

This paper presents a deep learning-based weight sensor, using optical speckle patterns of multimode fiber, designed for real-time intrusion detection. The weight sensor has been trained to identify 11 distinct speckle patterns, ranging in weight from 0.0 kg to 2.0 kg, with an interval of 200 g between each pattern. The estimation for untrained weights is based on the generalization capability of deep learning. This results in an average weight error of 243.8 g. Although this margin of error precludes accurate weight measurement, the system's ability to detect abrupt weight changes makes it a suitable choice for intrusion detection applications. The weight sensor is integrated with the Google Teachable Machine, and real-time intrusion notifications are facilitated by the ThingSpeak^TM cloud platform, an open-source Internet of Things (IoT) application developed by MathWorks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2848-2869
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• 2017

Mobile Wireless Sensor Networks (MWSN) are usually constrained in energy supply, which makes energy efficiency a key factor to extend the network lifetime. The management of the network topology has been widely used as a mechanism to enhance the lifetime of wireless sensor networks (WSN), and this work presents an alternative to this. Software Defined Networking (SDN) is a well-known technology in data center applications that separates the data and control planes during the network management. This paper proposes a solution based on SDN that optimizes the energy use in MWSN. The network intelligence is placed in a controller that can be accessed through different controller gateways within a MWSN. This network intelligence runs a Topology Control (TC) mechanism to build a backbone of coordinator nodes. Therefore, nodes only need to perform forwarding tasks, they reduce message retransmissions and CPU usage. This results in an improvement of the network lifetime. The performance of the proposed solution is evaluated and compared with a distributed approach using the OMNeT++ simulation framework. Results show that the network lifetime increases when 2 or more controller gateways are used.

• Journal of Sensor Science and Technology
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• v.27 no.3
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• pp.199-203
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• 2018

In this study, we develop a methane sensor module that can detect low concentrations below 5,000 ppm and measure up to the detection limit of 50 ppm with the NDIR method, with a long lifetime and high accuracy. Methane ($CH_4$) is one of a representative greenhouse gas, which is very explosive. Thus, it is important to quickly and accurately measure methane concentration in the air. To adjust the methane sensor for industrial field applications, a NDIR-based small sensor was implemented and characterized, where its volume was $4cm{\times}4cm{\times}2cm$ and its response time ($T_{90}$) was less than 30 sec. These results demonstrate that the proposed sensor is commercially available for low-concentration measurement, low volume, and fast response application, such as IoT sensor nodes and portable devices.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.47-54
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• 2022

In this paper, MEMS embedded system design implemented the sensor events via analyzing the characteristics that dynamically happened to an abnormal status in power IoT environments in order to guarantee a maintainable operation. We used three kinds of tools in this paper, at first Bluetooth Low Energy (BLE) technology which is a suitable protocol that provides a low data rate, low power consumption, and low-cost sensor applications. Secondly LSM6DSOX, a system-in-module containing a 3-axis digital accelerometer and gyroscope with low-power features for optimal motion. Thirdly BM1422AGMV Digital Magnetometer IC, a 3-axis magnetic sensor with an I2C interface and a magnetic measurable range of ±120 uT, which incorporates magneto-impedance elements to detect the magnetic field when the current flowed in the power devices. The proposed MEMS system was developed based on an nRF5340 System on Chip (SoC), previously compared to the standalone embedded system without bluetooth technology via mobile App. And also, MEMS embedded system with BLE 5.0 technology broadcasted the MEMS system status to Android mobile server. The experiment results enhanced the performance of MEMS system design by combination of sensors, BLE technology and mobile application.

• The Korean Journal of Community Living Science
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• v.28 no.3
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• pp.377-390
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• 2017

This study identified technological trends through an analysis of patents for the industrialization of personal protection and convenience equipment using information and communication technology (ICT) as a part of efforts to prevent farm work-related disasters. The analysis was conducted on patents registered and published between January 1974 and May 2016 by the world's five largest intellectual property offices, including the KIPO, USPTO, JPO, EPO, and SIPO. The results of the analysis indicate that the US (36.8%) and South Korea (30.9%) led technological research and development (R&D) with frequent patent applications. An analysis of the technological market revealed that these countries are in the growth and maturity stages, in which the number of patents and number of patent applicants grow rapidly. In terms of the technological market shares of major countries, the US recorded the highest market shares in the field of sensing systems for workers' dangerous conditions and convenience protection equipment based on the internet of things (IoT) convergence. South Korea marked the highest share of 41.8% in the field of sensing devices for dangerous conditions in the working environment. An analysis of the trend of patent applications by specific technologies disclosed the following results: sensing systems for workers' dangerous conditions accounted for the highest share (49.2%), followed by IoT convergence-based convenience protection equipment (26.3%) and sensing devices for dangerous conditions in the working environment (24.6%). Based on this study, ICT-based personal protection and convenience equipment technologies are expected to be actively developed in the future. It will be necessary to secure national competitiveness through R&D investments and commercialization in personal protection and convenience equipment appropriate for farm work as well as through the acquisition of patent technologies and intellectual property rights.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.33-39
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• 2017

In mission-critical applications such as vehicular networks, distributed robotics, and other cyber-physical systems, the requirements for latency are more stringent than traditional applications. Among them, autonomous V2V communication is a rapidly emerging domain of applications with a few milliseconds' latency requirements. Today's systems utilizing 802.11p or LTE-direct standards are not primarily designed for ultra-low latency. Because the medium access function contributes to a significant portion of the total latency, it is necessary to modify Layer2 in order to solve the problem. Focusing on MAC layer, we developed a scalable and latency-guaranteed MAC by devising Autonomous TDMA (ATDMA) in which autonomous joining/leaving is allowed without scheduling by coordinator. We also evaluated the performance of the algorithm by comparing with the WAVE protocol.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.1-9
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• 2018

The purpose of this exploratory study is to identify the mobile phone applications (apps) used by foreign tourists visiting South Korea through a pilot study using focus groups and individual interviews. Concentrating on tourist mobile app use in a smart tourism environment and categorized through a taxonomy of mobile applications lays the framework and determines the factors boosting tourism smartphone app trends by foreign tourists visiting South Korea. Researchers collected data through ethnographic methods and analyzed it through qualitative research to uncover major themes within the smart tourism app use phenomenon. The researchers coded, counted, analyzed, and then divided the findings gleaned from a pilot study and interviews into a taxonomy of seven logical smartphone app categories. The labeling and coding of all the data accounting for similarities and differences can be recognized and are logically discussed in the implications of the apps used by tourists to assist tourist destinations. More specifically these findings will assist smart tourism destinations by better understanding foreign tourist smartphone app use behavior. Tourists visiting South Korea interviewed in this study exhibited significant mastery of Internet of Things (IoT) technologies, craved free WiFi access, and utilized smartphone apps for all facets of their travel. Findings show major concentrations of app use in bookings of accommodations, tourist attractions, online shopping, navigation, wayfinding, augmented reality, information searching, language translation, gaming, and online dating while traveling in South Korea.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.