• Journal of Information Processing Systems
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• v.19 no.5
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• pp.602-613
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• 2023

High-quality image datasets are in high demand for various applications. With many online sources providing manually collected datasets, a persisting challenge is to fully automate the dataset collection process. In this study, we surveyed an automatic image dataset generation field through analyzing a collection of existing studies. Moreover, we examined fields that are closely related to automated dataset generation, such as query expansion, web scraping, and dataset quality. We assess how both noise and regional search engine differences can be addressed using an automated search query expansion focused on hypernyms, allowing for user-specific manual query expansion. Combining these aspects provides an outline of how a modern web scraping application can produce large-scale image datasets.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.149-158
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• 2024

Global Navigation Satellite System (GNSS) receivers are becoming increasingly sophisticated, equipped with advanced features and precise specifications, thus demanding efficient and high-performance hardware platforms. This paper presents the design and implementation of a Field-Programmable Gate Array (FPGA)-based GNSS receiver development platform for multi-band signal processing. This platform utilizes a FPGA to provide a flexible and re-configurable hardware environment, enabling real-time signal processing, position determination, and handling of large-scale data. Integrated signal processing of L/S bands enhances the performance and functionality of GNSS receivers. Key components such as the RF frontend, signal processing modules, and power management are designed to ensure optimal signal reception and processing, supporting multiple GNSS. The developed hardware platform enables real-time signal processing and position determination, supporting multiple GNSS systems, thereby contributing to the advancement of GNSS development and research.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• The Korean Journal of Medicine
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• v.99 no.3
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• pp.123-126
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• 2024

The amendment to the Higher Education Act enforced on February 20, 2024, abolishing the traditional 2-year pre-med and 4-year medical school programs, marks a significant shift in medical education in Korea. The academic system reform is expected to be a driving force for large-scale curriculum revision, presenting an opportunity to introduce new educational innovations not only in curriculum but also in student assessment and support systems. Addressing these challenges requires collaborative efforts among educators, students, and communities to navigate the evolving landscape of medical education effectively. In this regard, I will illustrate the recruitment and development of educators to implement the reform and the collaboration between communities and medical schools to innovate medical education.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.5
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• pp.211-218
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• 2024

As computing technology advances, many services, including AI, that previously operated in the cloud will become usable on devices that users carry. The emergence of ultra-high-speed mobile networks like 5G dramatically increases the utility of numerous devices in the real world. In the future, with technologies like deviceless computing, the range of applications will diversify even further, and demand will continue to grow. Consequently, the importance of technology for monitoring vast amounts of device information and deploying AI services tailored to the functions and performance of each device is becoming increasingly evident. Therefore, this paper proposes a large-scale edge device monitoring technique necessary to leverage simple sensors and low-spec, low-resource devices in conjunction with Multi-access Edge Computing (MEC) to provide various AI functionalities.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.326-337
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• 2024

The thermal runaway phenomenon in lithium-ion batteries hinders their large-scale application and leads to safety issues, including smoke, fire, and explosion. Therefore, early warning systems must be employed rapidly and reliably to ensure user safety, with methods for detecting gases such as hydrogen, carbon monoxide, and hydrocarbons-all indicators of the thermal runaway process-considered a promising approach. In particular, metal-oxide-semiconductor-based gas sensors can be used to monitor target gases owing to their high response, fast response, and facile integration. In this paper, we review various strategies for enhancing the performance of metal-oxide-semiconductor-based gas sensors, including nanostructure design, catalyst loading, and composite design. Future perspectives on employing metal-oxide-semiconductor-based gas sensors to monitor thermal runaway in lithium-ion batteries are also discussed.

• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.247-256
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• 2012

It's not easy for medium or large sized libraries to effectively manage their vast array of books and media data. Recently, in place of magnetic stripes and barcodes, RFID technology has been applied on a small scale to simple book management and theft-prevention initiatives. The development of RFID and USN applied systems and technology has led to RFID and USN being used in a diverse range of industrial fields, including book management systems in libraries. Using the aforementioned technology, the intelligent book management system suggested in this thesis can provide a more practical, effective, content-rich and convenient book management system.

• ETRI Journal
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• v.39 no.4
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• pp.582-591
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• 2017

To address the wire complexity problem in large-scale globally asynchronous, locally synchronous systems, a current-mode ternary encoding scheme was devised for a two-phase asynchronous protocol. However, for data transmission through a very long wire, few studies have been conducted on reducing the long propagation delay in current-mode circuits. Hence, this paper proposes a current steering logic (CSL) that is able to minimize the long delay for the devised current-mode ternary encoding scheme. The CSL creates pulse signals that charge or discharge the output signal in advance for a short period of time, and as a result, helps prevent a slack in the current signals. The encoder and decoder circuits employing the CSL are implemented using $0.25-{\mu}m$ CMOS technology. The results of an HSPICE simulation show that the normal and optimal mode operations of the CSL achieve a delay reduction of 11.8% and 28.1%, respectively, when compared to the original scheme for a 10-mm wire. They also reduce the power-delay product by 9.6% and 22.5%, respectively, at a data rate of 100 Mb/s for the same wire length.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.505-509
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• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Asian review of World Histories
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• v.4 no.1
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• pp.133-163
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• 2016

This essay critically analyses the legacy of Eisenstadt's The Political Systems of Empires for the comparative political history of pre-industrial empires. It argues that Eisenstadt has given us a rich toolkit to conceptualize the formation, maintenance, and dissolution of empires by theorizing the structural relationships between social groups in large-scale polities and among such polities, and by analysing global patterns of development in the distribution of the sources of social power. The Political Systems of Empires provides an inventory of key questions and dynamics that a comparative history of power relationships in empires cannot ignore. This essay, furthermore, discusses three methodological problems in Eisenstadt's work which have had a significant impact on comparative empire studies between the 1980s and the 2000s. The essay argues that certain shared features of comparative studies of pre-industrial empires help perpetuate Eurocentric analyses: the foregrounding of select empires and periods as ideal types (typicality), the focus on macro-historical structures and dynamics without the integration of social relationships and actions in historical conjunctures (the lack of scalability), and the search for convergence and divergence. These features need to be overcome to make Eisenstadt's legacy viable for comparative political history.