• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.147-147
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• 2021

A partition of water distribution network (WDN) into district metered areas (DMAs) brings the efficiency and efficacy for water network operation and management (O&M), especially in monitoring pressure and leakage. Traditionally, the DMA configurations (i.e., number, shape, and size of DMAs) are permanent and cannot be changed occasionally. This leads to changes in water quality and reduced network redundancy lowering network resilience against abnormal conditions such as water demand variability and mechanical failures. This study proposes a framework to automatically divide a WDN into dynamic DMA configurations, in which the DMA layouts can self-adapt in response to abnormal scenarios. To that aim, a complex graph theory is adopted to sectorize a WDN into multiscale DMA layouts. Then, different failure-based scenarios are investigated on the existing DMA layouts. Here, an optimization-based model is proposed to convert existing DMA layouts into dynamic layouts by considering existing valves and possibly placing new valves. The objective is to minimize the alteration of flow paths (i.e., flow direction and velocity in the pipes) while preserving the hydraulic performance of the network. The proposed method is tested on a real complex WDN for demonstration and validation of the approach.

• ETRI Journal
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• 제29권2호
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• pp.255-257
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• 2007

We propose an improved selective randomized load balancing (ISRLB) robust scheme under the hose uncertainty model for a special double-hop routing network architecture. The ISRLB architecture maintains the resilience properties of Valiant's load balancing and reduces the network cost/propagation delay in all other robust routing schemes.

• Journal of Communications and Networks
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• 제8권4호
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• pp.378-390
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• 2006

Many distributed applications build overlays on top of the Internet. Several unsolved issues at the network layer can explain this trend to implement network services such as multicast, mobility, and security at the application layer. On one hand, overlays creating basic topologies are usually limited in flexibility and scalability. On the other hand, overlays creating complex topologies require some form of application level addressing, routing, and naming mechanisms. Our aim is to design an efficient and robust addressing, routing, and naming infrastructure for these complex overlays. Our only assumption is that they are deployed over the Internet topology. Applications that use our middleware will be relieved from managing their own overlay topologies. Our infrastructure is based on the separation of the naming and the addressing planes and provides a convergence plane for the current heterogeneous Internet environment. To implement this property, we have designed a scalable distributed k-resilient name to address binding system. This paper describes the design of our overlay infrastructure and presents performance results concerning its routing scalability, its path inflation efficiency and its resilience to network dynamics.

• 정보처리학회논문지C
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• 제14C권5호
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• pp.403-410
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• 2007

효율적인 센서 네트워크 통신이 가능하기 위해서는 보안을 제공하는 라우팅 메커니즘이 필수적이다. 본 연구에서는 안전한 데이터 통신이 가능하도록 하기위해 센서 네트워크 필드를 육각형의 클러스터로 사전에 나누고 가우시안 분포에 따라 각 클러스터에 노드를 배치한 다음 각 클러스터의 클러스터헤드와 게이트웨이 노드를 통해 센서 노드가 감지한 정보를 클러스터헤드론 통해 모아서 보안 정보와 함께 베이스 스테이션에 전달하는 메커니즘을 제안한다. 제안 메커니즘은 전체적인 오버헤드를 효과적으로 줄이면서 효율성을 보장할 뿐 아니라 다양한 라우팅 공격에 대한 저항성을 갖는다.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• 한국전자통신학회논문지
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• 제10권7호
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• pp.769-774
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• 2015

사물인터넷의 하부 구조로 센서네트워크는 네트워크의 신뢰성이 중요한 이슈이다. 센서네트워크의 각 노드는 제한된 메모리와 파워로 인해 데이터의 신뢰성을 향상시켜야 한다. 본 연구에서는 센서 노드가 생성한 데이터를 이웃 노드에 복제하는 기법을 제안한다. 네트워크의 신뢰성을 향상시키기 위해서 주변 노드의 메모리 및 파워 잔량이 높은 노드에 사본을 저장하는데 1 홉 이상의 노드들에도 저장하여 공간적인 재해에도 대비한다. 실험을 통해 적절한 사본을 유지하는 것은 네트워크의 수명에 부정적인 영향을 미치지 않으면서도 사물인터넷의 신뢰성을 향상시킴을 확인하였다.

• 한국인공지능학회지
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• 제11권2호
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• pp.1-6
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• 2023

This paper presents a comprehensive scheme for assessing the importance of multiple microgrids (MGs) network that includes distributed energy resources (DERs), renewable energy systems (RESs), and energy storage system (ESS) facilities. Due to the uncertainty of severe weather, large-scale cascading failures are inevitable in energy networks. making the assessment of the structural vulnerability of the energy network an attractive research theme. This attention has led to the identification of the importance of measuring energy nodes. In multiple MG networks, the energy nodes are regarded as one MG. This paper presents a modified PageRank algorithm to assess the importance of MGs that include multiple DERs and ESS. With the importance rank order list of the multiple MG networks, the core MG (or node) of power production and consumption can be identified. Identifying such an MG is useful in preventing cascading failures by distributing the concentration on the core node, while increasing the effective link connection of the energy flow and energy trade. This scheme can be applied to identify the most profitable MG in the energy trade market so that the deployment operation of the MG connection can be decided to increase the effectiveness of energy usages. By identifying the important MG nodes in the network, it can help improve the resilience and robustness of the power grid system against large-scale cascading failures and other unexpected events. The proposed algorithm can point out which MG node is important in the MGs power grid network and thus, it could prevent the cascading failure by distributing the important MG node's role to other MG nodes.

• 한국통신학회논문지
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• 제31권8C호
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• pp.777-785
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• 2006

H.264는 향상된 압축 성능과 에러 복구 기술, 네트워크로의 적응력을 포함하고 있는 비디오 압축 표준으로, 실시간 비디오 스트리밍, 디지털 멀티미디어 방송 등의 여러 분야에서 적용되고 있다. 인터넷 상으로 비디오를 전송할 경우 압축된 비디오 스트림은 패킷 손실에 약하므로 에러 복구 기술과 에러 은닉 기술은 필수적인 요소이다. 본 논문은 먼저 움직임이 상대적으로 적을 경우 H.264의 인트라 프레임을 위한 에러 은닉 성능을 향상시킬 수 있는 간단한 시간방향 에러 은닉 방법을 제안한다. 그리고 제안된 시간방향 에러 은닉 방식과 H.264의 인트라 프레임을 위해서 사용하고 있는 공간방향 에러 은닉 방식을 적응적으로 결합한, H.264의 인트라 프레임을 위한 하이브리드 에러 은닉 방법을 제안한다. 패킷이 손실되는 환경에서 시뮬레이션을 했을 때, 제안된 하이브리드 에러은닉 방법은 H.264의 인트라 프레임에 사용되는 기존의 공간방향 에러 은닉 방법에 비하여, 약 0.5-4dB 정도의 PSNR 성능 향상을 보인다.

• Safety and Health at Work
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• 제11권2호
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• pp.127-135
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• 2020

A safe work environment is crucial in high-risk industries, such as construction refurbishment. Safety incidents caused by uncertainty and unexpected events in construction refurbishment systems are difficult to control using conventional safety management techniques. Resilience engineering (RE) is proposed as an alternative to traditional safety management approaches. It presents a successful safety management methodology designed to deal with uncertainty in high-risk work environments. Despite the fact that RE resides in the safety domain, there is no common set of RE indicators to measure and assess resilient in the work environment. The main aim of this research is to explore RE indicators that have been identified as important in developing and assessing the resilient work environment in high-risk industries, particularly in construction refurbishment. Indicators have been attained through a systematic literature review of research and scholarly articles published between the years 2004 and 2019. The literature review explored RE indicators in various industries. Descriptive analysis and co-occurrence-based network visualization were used for data analysis. The findings revealed 28 RE indicators in 11 different high-risk industries. The results show that the four commonly used indicators were: top-management commitment, awareness, learning, and flexibility, all of which have a strong relationship with RE. The findings of this study are useful for stakeholders when making decisions concerning the most important RE indicators in the context of their research or practice as this would avoid the ambiguity and disparity in the identification of RE indicators.