• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1565-1568
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with multi-layer neural network. The structure of modular neural network in researched by Jacobs and Jordan is selected in this paper. Modular network consists of several expert networks and a gating network which is composed of single-layer neural network or multi-layer neural network. We propose modular network structure using recurrent neural network, since the state of the whole network at a particular time depends on an aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.694-697
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with Multi-Layer Neural Network(MLNN). The structure of Modular Neural Network(MNN) in researched by Jacobs and jordan is selected in this paper. Modular network consists of several Expert Networks(EN) and a Gating Network(CN) which is composed of single-layer neural network(SLNN) or multi-layer neural network. We propose modular network structure using Recurrent Neural Network(RNN), since the state of the whole network at a particular time depends on aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• Land and Housing Review
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• v.10 no.3
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• pp.67-75
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• 2019

The modular construction means that more than 70% of the parts such as walls, windows, electrical wiring, facility piping, bathrooms, and kitchen appliances are pre-assembled at the factory and transported to the site. It is possible to shorten the construction period than general construction work and to secure high quality through modular mass production since the modular construction works in the field at the same time as the modular production. However, there are only four domestic modular manufacturers, and each company's modular components and construction methods are different, so it is necessary to standardize them. Therefore, this study investigated the construction process centering on the stacking method of modular housing construction work applied to D site in Cheonan-si, and proposed the key points of quality management by construction stage. As the project was conducted as a pilot project for government R&D projects, some differences may occur from general modular housing construction. However, the construction process and quality control focus of each unit box type modular house analyzed in this study can be used as basic data in the future of modular housing construction. In addition, the results of this study can be used to establish construction standards, such as the development of checklists and establishment of standard processes.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.55-62
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• 2016

In this paper, we propose a hybrid multi.system-on-chip (H-MSoC) architecture that provides a high-flexibility system in a rapid development time. The H-MSoC approach provides a flexible system-on-chip (SoC) architecture that is easy to configure for physical- and application-layer development. The physical- and application-layer aspects are dynamically designed and modified; hence, it is important to consider a design methodology that supports rapid SoC development. Physical layer development refers to intellectual property cores or other modular hardware (HW) development, while application layer development refers to user interface or application software (SW) development. H-MSoC is built from multi-SoC architectures in which each SoC is localized and specified based on its development focus, either physical or application (hybrid). Physical HW development SoC is referred to as physical-SoC (Phy-SoC) and application SW development SoC is referred to as application-SoC (App-SoC). Phy-SoC and App-SoC are connected to each other via Ethernet. Ethernet was chosen because of its flexibility, high speed, and easy configuration. For prototyping, we used a LEON3 SoC as the Phy-SoC and a ZYNQ-7000 SoC as the App-SoC. The proposed design was proven in real-time tests and achieved good performance.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.267-271
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• 1989

최근 Scofield는 coulomb energy network에 적용할 수 있는 learning algorithm(supervised learning algorithm)을 제안하였다. 이 learning algorithm은 multi-layer network에도 쉽게 적용이 가능하고 한 layer 에서 발생한 error가 다른 layer에 영향을 주지 않아서 system을 modular하게 구성할 수가 있으며 각 layer를 독립적으로 learning 시킬 수 있는 특징이 있다. 본 논문에서는 coulomb energy network를 이용하여 한글인식을 위한 neural network를 구현하여 인식실험을 한 결과와 구현한 network 에서 인식율을 높이기 위한 방안 (2 stage learning) 을 제시한다.

• ETRI Journal
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• v.41 no.6
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• pp.863-872
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• 2019

Elliptic curve cryptography is a relatively lightweight public-key cryptography method for key generation and digital signature verification. Some lightweight curves (eg, Curve25519 and Curve Ed448) have been adopted by upcoming Transport Layer Security 1.3 (TLS 1.3) to replace the standardized NIST curves. However, the efficient implementation of Curve Ed448 on Internet of Things (IoT) devices remains underexplored. This study is focused on the optimization of the Curve Ed448 implementation on low-end IoT processors (ie, 8-bit AVR and 16-bit MSP processors). In particular, the three-level and two-level subtractive Karatsuba algorithms are adopted for multi-precision multiplication on AVR and MSP processors, respectively, and two-level Karatsuba routines are employed for multi-precision squaring. For modular reduction and finite field inversion, fast reduction and Fermat-based inversion operations are used to mitigate side-channel vulnerabilities. The scalar multiplication operation using the Montgomery ladder algorithm requires only 103 and 73 M clock cycles on AVR and MSP processors.

• BioChip Journal
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• v.12 no.4
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• pp.280-286
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• 2018

Hydrogel scaffolds composed of multiple components are promising platform in tissue engineering as a transplantation materials or artificial organs. Here, we present a new fabrication method for implementing multi-layered macroscopic hydrogel scaffold composed of multiple components by controlling height of hydrogel layer through precise control of ultraviolet (UV) energy density. Through the repetition of the photolithography process with energy control, we can form several layers of hydrogel with different height. We characterized UV energy-dependent profiles with single-layered PEGDA posts photocrosslinked by the modular methodology and examined the optical effect on the fabrication of multi-layered, macroscopic hydrogel structure. Finally, we successfully demonstrated the potential applicability of our approach by fabricating various macroscopic hydrogel constructs composed of multiple hydrogel layers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.56-68
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• 2003

Difficult problems In implementing digital neural network hardware are the extension of synapses and the programmability for relocating neurons. In this paper, the structure of a new hardware is proposed for solving these problems. Our structure based on traditional SIMD can be dynamically and easily reconfigured connections of network without synthesizing and mapping original design for each use. Using additional modular processing unit the numbers of neurons find synapses increase. To show the extensibility of our structure, various models of neural networks : multi-layer perceptrons and Kohonen network are formed and tested. The performance comparison with software simulation shows its superiority in the aspects of performance and flexibility.

• Journal of IKEEE
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• v.27 no.4
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• pp.430-438
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• 2023

The Modular Multilevel Converter (MMC) has emerged as a key component in HVDC systems due to its ability to efficiently transmit large amounts of power over long distances. In such systems, accurate estimation of the MMC capacitor voltage is of utmost importance for ensuring optimal system performance, stability, and reliability. Traditional methods for voltage estimation may face limitations in accuracy and robustness, prompting the need for innovative approaches. In this paper, we propose a novel distributed neural network observer specifically designed for MMC capacitor voltage estimation. Our observer harnesses the power of a multi-layer neural network architecture, which enables the observer to learn and adapt to the complex dynamics of the MMC system. By utilizing a distributed approach, we deploy multiple observers, each with its own set of neural network layers, to collectively estimate the capacitor voltage. This distributed configuration enhances the accuracy and robustness of the voltage estimation process. A crucial aspect of our observer's performance lies in the meticulous initialization of random weights within the neural network. This initialization process ensures that the observer starts with a solid foundation for efficient learning and accurate voltage estimation. The observer iteratively updates its weights based on the observed voltage and current values, continuously improving its estimation accuracy over time. The validity of proposed algorithm is verified by the result of estimated voltage at each observer in capacitor of MMC.