• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.540-542
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• 2014

UPnP(Universal Plug and Play) 규격은 홈 네트워크 환경에서 네트워크 기능이 지원되는 다양한 기기들을 발견하고 상호 서비스 연동이 가능하게 해준다. 최근 모바일 컴퓨팅, 클라우드 기반 서비스, 스마트 기기 콘텐츠 공유, 사물인터넷(IoT - Internet of Things) 등과 같은 IT 패러다임이 부각되고 있으나 이들을 지원하기에는 홈 네트워크에 국한된 UPnP는 기능적인 한계를 보여주었다. 이와 같은 새로운 IT 패러다임을 지원하기 위해 최근 UPnP 포럼은 기존 UPnP를 확장시켜 UPnP+를 발표하였다. 본 논문에서는 UPnP+의 기반이 되는 UDA 2.0(UPnP Device Architecture V2.0) 규격의 상세 기능을 소개하고, 어떻게 UDA 2.0이 홈 네트워크의 영역을 광대역 망과 비IP 기기의 영역까지 확장시킬 수 있는지 보여준다.

• International journal of advanced smart convergence
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• 제8권3호
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• pp.69-77
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• 2019

Today, most of elevators have an emergency call facility for emergency situations. However, if the network installed in the elevator is also out of power, it cannot be used for the elevator remote monitoring and management. So, we develop an integrated and unified emergency call system, which can transmit not only telephone call but also data signals using PSTN(Public Switched Telephone Network) in order to remote monitoring and management of elevators, even though a power outage occurs. The proposed integrated emergency call system to process multiple data such as voice and operational information is a multi-channel board system which is composed of an emergency phone signal processing module and an operational information processing module in the control box of elevator. In addition, the RMS(remote management server) systems based on the Web consist of a dial-up server and a remote monitoring server where manages the elevator's operating information, status records, and operational faults received via the proposed integrated and unified emergency call system in real time. So even if there's a catastrophic emergency, the proposed RMS systems shall ensure and maintain the safety of passengers inside the elevator. Also, remote control of the elevator by this system should be more efficient and secure. In near future, all elevator emergency call system need to support multifunctional capabilities to transmit operational data as well as phone calls for the safety of passengers. In addition, for safer elevators, it is necessary to improve them more efficiently by combining them with high-tech technologies such as the Internet of Things and artificial intelligence.

• 한국경제지리학회지
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• 제24권3호
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• pp.243-258
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• 2021

본 연구는 미·중 무역 전쟁과 일대일로가 촉발할 세계무역의 변화를 부가가치 기준 무역 통계를 이용하고, 사회연결망 기법을 활용하여 분석하였다. 분석 결과, 첫째, 일대일로 무역 환경은 제조 허브로서의 중국의 위상을 견고히 하고, 유럽 서비스 산업의 위상을 강화한다. 둘째, 1995년부터 2011년까지 GVC 네트워크 내에서 영향력이 큰 산업인 미국의 R&D 산업과 도소매업 및 독일의 자동차 산업은 미·중 무역 분쟁과 일대일로 무역 환경이 반영된 2049년까지도 그 위상이 안정적으로 유지된다. 셋째, 커뮤니티 수의 증가를 통해 일대일로가 GVC 네트워크의 분화를 심화시킨다는 것을 확인할 수 있다. 끝으로 중국의 전자 산업, 독일의 자동차 산업, 미국의 R&D 산업의 커뮤니티 진화 형태에서 주목할 만한 특성이 발견되는데 이는 각국 서비스 산업의 역량과 밀접하게 연관된다.

• 정보보호학회논문지
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• 제16권1호
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• pp.105-114
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• 2006

USN은 모든 사물에 컴퓨팅과 통신기능 및 센싱 기능을 부여하여 언제, 어디서나, 통신이 가능한 환경을 구축하는 네트워크로서, 향후에는 다양한 센싱 기능이 추가되어 이들 간의 네트워크가 구축되는 형태로 발전할 것이다. 따라서 본 논문에서는 무선 센서 네트워크를 무인 경비 시스템에 적용할 경우 나타날 수 있는 정보 보안상의 취약점을 도출하고, 현재 제안된 보안 프로토콜 중에서 SNEP(Secure Network Encryption Protocol)을 사용하여 안전한 무선 센서네트워크를 만들고자 한다. 그러나 SNEP에 사용한 CBC-MAC은 메시지의 길이가 가변일 경우 안전하지 못하기 때문에, 오직 한 개의키를 갖으면서도 임의의 길이 메시지도 안전하게 취급할 수 있는 OMAC(One-Key CBC-MAC)를 SNEP에 적용한 새로운 기법인 OMAC-SNEP을 제안하고, 구현하였다. 따라서 본 OMAC-SNEP 기법은 무인 경비시스템은 물론 기타의 무선 센서 네트워크에서도 널리 활용이 가능할 것이라 생각된다.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.191-200
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• 2022

Constrained Application Protocol (CoAP) is a standardized protocol by the Internet Engineering Task Force (IETF) for the Internet of things (IoT). IoT devices have limited computation power, memory, and connectivity capabilities. One of the significant problems in IoT networks is congestion control. The CoAP standard has an exponential backoff congestion control mechanism, which may not be adequate for all IoT applications. Each IoT application would have different characteristics, requiring a novel algorithm to handle congestion in the IoT network. Unnecessary retransmissions, and packet collisions, caused due to lossy links and higher packet error rates, lead to congestion in the IoT network. This paper presents an adaptive congestion control protocol for CoAP, Adaptive Congestion Control with a Backoff algorithm (ACCB). AACB is an extension to our earlier protocol AdCoCoA. The proposed algorithm estimates RTT, RTTVAR, and RTO using dynamic factors instead of fixed values. Also, the backoff mechanism has dynamic factors to estimate the RTO value on retransmissions. This dynamic adaptation helps to improve CoAP performance and reduce retransmissions. The results show ACCB has significantly higher goodput (49.5%, 436.5%, 312.7%), packet delivery ratio (10.1%, 56%, 23.3%), and transmission rate (37.7%, 265%, 175.3%); compare to CoAP, CoCoA+ and AdCoCoA respectively in linear scenario. The results show ACCB has significantly higher goodput (60.5%, 482%,202.1%), packet delivery ratio (7.6%, 60.6%, 26%), and transmission rate (40.9%, 284%, 146.45%); compare to CoAP, CoCoA+ and AdCoCoA respectively in random walk scenario. ACCB has similar retransmission index compare to CoAp, CoCoA+ and AdCoCoA respectively in both the scenarios.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• 방송공학회논문지
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• 제28권1호
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• pp.90-99
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• 2023

본 연구에서는 전통적인 RGB 영상보다 데이터양이 적은 로우 센서 영상을 이용한 초해상화 네트워크를 제안하고 이에 대한 실험결과를 정리하였다. 로우 센서 영상의 초해상화는 일반적인 RGB 초해상화와 달리 카메라에서 일어나는 후처리 가공이 없는 무손실영상을 이용하기 때문에 결과물의 성능이 일반 RGB 초해상화 연구와 달리 색상 보정에 따라 많이 좌우된다. 따라서, 본 연구에서는색상 보정을 위한 모듈을 개발하여 기존 RGB 기반 네트워크에 삽입해 이를 이용해 성능 결과를 비교하였다. 연구 결과 색상 보정 모듈을 적용함으로 성능 지표의 향상이 있음을 확인하였다. 다만, 출력 영상의 의도하지 않은 아티팩트가 발생하는 현상을 확인하였고, 성능 지표 중 PSNR의 향상이 분명하나 SSIM의 성능이 일부 떨어지는 것으로 확인하였다.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• International Journal of Automotive Technology
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• 제4권1호
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• pp.47-55
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• 2003

The Center for Transportation Research at the Argonne National Laboratory (ANL) supports the DOE by evaluating advanced automotive technologies in a systems context. ha has developed a unique set of compatible simulation tools and test equipment to perform an integrated systems analysis project from modeling through hardware testing and validation. This project utilized these capabilities to demonstrate the trade-off in fuel economy and Oxides of Nitrogen (NOx) emissions in a so-called ‘pre-transmission’ parallel hybrid powertrain. The powertrain configuration (in simulation and on the dynamometer) consists of a Compression Ignition Direct Ignition (CIDI) engine, a Continuously Variable Transmission (CVT) and an electric drive motor coupled to the CVT input shaft. The trade-off is studied in a simulated environment using PSAT with different controllers (fuzzy logic and rule based) and engine models (neural network and steady state models developed from ANL data).

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.313-316
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• 2002

This paper presents the design and implementation of a crypto processor, a special-purpose microprocessor optimized for the execution of cryptography algorithms. This crypto processor can be used fur various security applications such as storage devices, embedded systems, network routers, etc. The crypto processor consists of a 32-bit RISC processor block and a coprocessor block dedicated to the SEED and triple-DES (data encryption standard) symmetric key crypto (cryptography) algorithms. The crypto processor has been designed and fabricated as a single VLSI chip using 0.5 $\mu\textrm{m}$ CMOS technology. To test and demonstrate the capabilities of this chip, a custom board providing real-time data security for a data storage device has been developed. Testing results show that the crypto processor operates correctly at a working frequency of 30MHz and a bandwidth o1240Mbps.