• Nuclear Engineering and Technology
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• v.17 no.4
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• pp.247-255
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• 1985

This paper describes a new method for the design of variable structure model-following control systems(VSMFC). This design concept is developed using the theory of variable structure systems (VSS) and slide mode. The new results are presented on the sliding control methodology to achieve accurate tracking for a class of nonlinear, multi-input multi-output(MIMO), time varying systems in the presence of parameter variations. The design requires little computational effort. The dynamic response is insensitive to parameter variations. The feasibility and the advantages of the method are illustrated by applying it to a 1000 MWe boiling water reactor(BWR). The control is studied in the range of 85%∼90% of rated power for load-following control. A set of 12 nonlinear differential equations is used to simulate the total plant. A 6-th order linear model has been developed from these equations at 85% of rated power. The obtained controller is shown by simulations to be able to compensate for a plant parameter variation over a wide power range.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.33-39
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• 2005

The maximum output torque and power developed by the machine is ultimately depended on the allowable inverter current rating and maximum voltage which the inverter can supply to the machine. Therefore, considering the limited voltage and current capacities, it is desirable to consider a control method which yields the best possible torque per ampere. In this paper, we propose fuzzy neural network(FNN) controller that combines a fuzzy control and the neural network for high performance control of induction motor drive. This controller composes antecedence of the fuzzy rules and consequence by a clustering method and a multi-layer neural networks. This controller is compounding of advantages that robust control of a fuzzy control and high-adaptive control of the neural networks. Also, this paper is proposed control of maximum torque per ampere(MTPA) of induction moor. This strategy is reposed which is simple in structure and has the honest goal of minimizing the stator current magnitude for given load torque. The performance of the proposed induction motor drive with maximum torque control using FNN controller is verified by analysis results at dynamic operation conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.35-42
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• 2016

Modern aircraft is facing the increase of power demands and thermal challenges. In accordance with the application of more electric technology and advanced mission requirement, aircraft system requires increase of power generation and it cause increase of internal heat generation. Simultaneously, restrictions have significantly been imposed to the thermal management system. Modern aircraft must maintain low radar observability and infra-red signature. In addition, new composite aircraft skins have reduced the amount of heat that can be rejected to the environment. The combination of these characteristics has increased the challenges faced by thermal management. In order to mitigate the thermal challenges, the concept of system level thermal management should be applied and new modeling and simulation tools need to be developed. To develop and utilize system level thermal management technology, three key points are considered. Firstly, the performance changes of subsystems and components must be assessed at an integrated thermal system. It is because that each subsystem and component interacts with other subsystems or components and it can directly effects on overall system performance. Secondly, system level thermal management requirements and solutions must be evaluated early in conceptual design process as vehicle and propulsion system configuration decisions are being made. Finally, new component level thermal management technologies must focus on reducing heat generation and increasing the availability of heat sinks.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.135-142
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• 2009

With the development of ubiquitous computing technology, the number of HCI applications is increasing, where they utilize various contexts to provide adaptive services to users according to the change of contexts, and also, technologies for collecting various sensor data and generating integrated contexts get more important. However, the research on the collection and integration of multi-sensor data is not sufficient when we consider the various utilization areas of the integrated contexts. In particular, they have some problems to be solved such as duplication of the context data and the high system load. In this paper, we propose an integrated context generation scheme based on Ant Colony System. Proposed scheme generates the context data as a form of XML and avoids the generation of unnecessary context information by detecting the repeated sensor information based on the ant colony system. As a result of detections, we reduce wasted resources and repositories when the integrated context is created. We also reduce the overhead for reasoning.

• Journal of Power Electronics
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• v.18 no.1
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• pp.234-250
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• 2018

This paper presents a new load sharing control for use between paralleled three-phase inverters in an islanded microgrid based on the online line impedance estimation by the use of a Kalman filter. In this study, the mismatch of power sharing when the line impedance changes due to temperature, frequency, significant differences in line parameters and the requirements of the Plug-and-Play mode for inverters connected to a microgrid has been solved. In addition, this paper also presents a new droop control method working with the line impedance that is different from the traditional droop algorithm when the line impedance is assumed to be pure resistance or pure inductance. In this paper, the line impedance estimation for parallel inverters uses the minimum square method combined with a Kalman filter. In addition, the secondary control loops are designed to restore the voltage amplitude and frequency of a microgrid by using a combined nominal value SOGI-PLL with a generalized integral block and phase lock loop to monitor the exact voltage magnitude and frequency phase at the PCC. A control model has been simulated in Matlab/Simulink with three voltage source inverters connected in parallel for different ratios of power sharing. The simulation results demonstrate the accuracy of the proposed control method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.123-129
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• 2016

In this paper, results of produced PCU(Power Control Unit) prototype was showed by suggesting and maintaining optimal operation status which let the three functional modules automatically operate with its necessity by prioritizing operation process. In order to validate effectiveness of the suggested method, we produced a test PCU and examined the results. PCU consists of S3R(Sequential Switching Shunt Regulator), BCR(Battery Charge Regulator), and BDR(Battery Discharge Regulator): converting photovoltaic power into constant voltage at linked bus voltage; storing dump power in the battery which is an auxiliary energy storage device; and supplying power charged in battery to the load. To maintain its high reliability and optimal condition of these three power conversion modules, each module operates in parallel and stable bus voltage is required to be retained at all-time due to the nature of power supply for satellite.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.637-644
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• 2021

Dynamic response of structures can be evaluated experimentally by conducting cyclic loading tests. It has been known that steel materials are rate-dependent and the lateral response of a structure is significantly affected by the presence of axial force. However, the rate-dependency of steel column structures subjected to both axial and lateral loads has not been sufficiently studied yet due to the difficulty of controlling the axial force in a real-time manner during test. This study introduces an advanced way to apply the axial load in real-time to a column specimen using the adaptive time series (ATS) compensator and the flexible loading beam (FLB), where the H-shape steel columns made of SS275 are used for monotonic and cyclic loading tests with various loading rates with axial loads. The lateral strength and post-yield response of the steel columns are compared for each of monotonic and cyclic loading tests. The estimating equation of yield stress of various strain rate has proposed and finite element analysis were performed for comparison.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.