• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1418-1432
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• 1996

In addition t improved availability, replication of data can enhance performance of distributed real-time transaction processing system by allowing transactions initiated at multiple node to be processed concurrently. To satisfy both the consistency and real-time constraints, it is necessary to integrate concurrency control and atomic commitment protocols with time-driven scheduling methods. blocking caused by existing concurrency control protocols is incompatible with time-driven scheduling because they cannot schedule transactions to meet given deadlines. To maintain consistency of replicated data and to provide a high degree of schedulability and predictability , the proposed time-driven scheduling methods integrate optimistic concurrency control protocols that minimize the duration of blocking and produce the serialization by reflecting the priority transactions. The atomicity of transactions is maintained to ensure successful commitment in distributed environment. Specific time-driven scheduling techniqueare discussed, together with an analysis of the performance of this scheduling.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.459-463
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• 2001

We model and simulate master-driven TDD wireless communication systems, e.g., Bluetooth systems. We model the Bluetooth system and use the BONeS simulation tool to conduct event-drivers simulations. In order to support more than seven slave devices in a piconet, a park mode is considered and modeled. We evaluate the performance, i.e., throughput and delay, using simulations when multi-connections (bath ACL and SCO connections) are present in a piconet. We show that the data rate of ACL connections may be less than 20 kbps when SCO connection(s) and more than six ACL connections are jointly supported in a piconet. In addition, if up to five ACL connections are supported, the average delay is shown to be maintained less than 20 msec. Our results can serve as a guideline to the design of master-driven TDD wireless communication systems with performance requirements.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.83-88
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• 2002

This study is to provide the experimental information and basic data on heat transfer characteristics of horizontal liquid film driven by hot wind. Heat transfer characteristics of the liquid film in the rectangular duct was observed and the change of film temperature was measured. The experiments were carried out for a variety of parameter, such as feed water rate and velocity and temperature of feed air. From the observation and the measurement the general understanding of heat transfer characteristics for liquid film driven by hot wind was provided.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.277-288
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• 2001

Nowadays, for the competitive power of an enterprise, there are many attempts to implement information system that could support business innovation by business process re-engineering. However, there is no effort to standardize the core business processes of enterprise based on standards of data, documents. These facts make it difficult to introduce and implement enterprise information system designed by business processes of the higher level. Therefore, standardization of business process by analyzing the functionality and relationships among them are important and necessary. The results of our research are summarized as process-driven standardization (standardization of core business processes) and development of a repository. In process-driven standardization, we proposed the reference model by analyzing the business processes of the leading enterprises for core business processes. The reference model focuses on core business processes, such as sales management, procurement management, production management, logistics management, and customer support in manufacturing industry. We developed a knowledge-based system as a repository for a integrated management system of business process. And this repository was built up web-based system for the purpose of both reference and management.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.78-83
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• 2023

This paper proposes a lightweight trace-driven Processing-In-Memory (PIM) simulator, TP-Sim. TP-Sim is a General Purpose PIM (GP-PIM) simulator that evaluates various PIM system performance-related metrics. Based on instruction and memory traces extracted from the Intel Pin tool, TP-Sim can replay trace files for multiple models of PIM architectures to compare its performance. To verify the availability of TP-Sim, we estimated three different system configurations on the STREAM benchmark. Compared to the traditional Host CPU-only systems with conventional memory hierarchy, simple GP-PIM architecture achieved better performance; even the Host CPU has the same number of in-order cores. For further study, we also extend TP-Sim as a part of a heterogeneous system simulator that contains CPU, GPGPU, and PIM as its primary and co-processors.

• 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.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1276-1282
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• 2013

This paper proposes a stochastic modeling of plug-in electric vehicles (PEVs) distribution in power systems, and analyzes the corresponding clustering characteristic. It is essential for power utilities to estimate the PEV charging demand as the penetration level of PEV is expected to increase rapidly in the near future. Although the distribution of PEVs in power systems is the primary factor for estimating the PEV charging demand, the data currently available are statistics related to fuel-driven vehicles and to existing electric demands in power systems. In this paper, we calculate the number of households using electricity at individual ending buses of a power system based on the electric demands. Then, we estimate the number of PEVs per household using the probability density function of PEVs derived from the given statistics about fuel-driven vehicles. Finally, we present the clustering characteristic of the PEV distribution via case studies employing the test systems.

• Wind and Structures
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• v.15 no.3
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• pp.247-261
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• 2012

Some numerical investigations are presented concerning the response of a given plate under turbulence driven excitations. Three different input loads are simulated according to the wall pressure distributions derived from the models proposed by Corcos, Efimtsov and Chase, respectively. Modal solutions (finite element based) are used for building the modal stochastic responses in the sub-critical aerodynamic frequency range. The parametric investigations concern two different values of the structural damping and three values of the boundary layer thickness. A final comparison with available experimental data is also discussed. The results demonstrate that the selection of the adequate TBL input model is still the most critical step in order to get a good prediction.