• Nuclear Engineering and Technology
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• v.53 no.10
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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.

• Journal of Internet Technology
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• v.19 no.6
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• pp.1801-1810
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• 2018

In this study, an energy-aware data-replication is proposed to effectively support a mobile sink in a solar-powered wireless sensor network (WSN). By utilizing the redundant energy efficiently, the proposed scheme shares the gathered data among the cluster heads using a backbone network, in order to increase data-reliability. It also maintains a backup cluster head in each cluster to enhance topological resilience. The simulation result showed that, compared to conventional clustering techniques, the proposed scheme decreases the total amount of data loss from the mobile sink as well as saving its energy (by reducing its moving distance), while minimizing the unexpected blackout time of the sensor node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2464-2482
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• 2024

The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network's accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of-distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods when confronted with contextual bias on this dataset. Our findings indicate that encoding methods incorporating more detailed digit information exhibit greater resilience against contextual bias. Inspired by this finding, we propose a digit-aware position embedding strategy, and the experimental results demonstrate that this strategy is highly effective in improving the robustness of neural networks against contextual bias.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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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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• v.29 no.2
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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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• v.8 no.4
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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.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.403-410
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• 2007

For reliable sensor network communication, secure data transmission mechanisms are necessary. In our work, for secure communication, we cluster the network field in hexagonal shape and deploy nodes according to Gaussian distribution. After node deployment, clusterheads and gateway nodes in each cluster play the role of aggregating and delivering the sensed data with suity information all the way to the base station. Our mechanism decreases the overhead and provides food performance. It also has resilience against various routing attacks.

• Journal of information and communication convergence engineering
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• v.22 no.1
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.7
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• pp.769-774
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• 2015

Sensor network as an infrastructure of IoT(Internet of Things) has reliability issue because sensor nodes have limited memory as well as bounded battery. To improve the reliability of network, this paper proposes a data distribution scheme. The proposed algorithm distributes the data which each sensor node periodically produces into neighbor nodes that have enough memory as well as battery. This distribution process goes on more than 1 hop for overcoming unexpected spatial crash. Through simulation, we have confirmed that the proposed scheme can improve the resilience of IoT without affecting the life time of sensor network.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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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.