• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.800-811
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• 2018

This paper presents a regenerative braking control scheme for Switched Reluctance Machine (SRM) drive in Electric Vehicles (EVs). The main purpose is to maximize the recovered energy during battery charging by taking into account the nonlinear physical characteristics of the Switched Reluctance Machine. The proposed regenerative braking method employs the back-EMF in the generation process as a complicated position-dependent voltage source. The proposed maximum power recovery (MPR) operation of the regenerative braking is first based on the maximization of the extracted power from the machine and then the maximization of the power transferred to the battery. The maximum power extraction (MPE) from SRM is based on maximizing the energy conversion ratio by the calculation of the optimum PWM switching duty cycle, turn-on, and turn-off angles. By using the impedance matching theorem that allows the maximum power transfer (MPT) of the MPE, the proposed MPR is achieved. The parametric averaged value modeling of the machine phase currents in the chopping control mode is used for MPR realization. By following this model, a nonlinear equivalent input resistance is derived for the battery internal resistance matching. The effectiveness of the proposed regenerative braking method is demonstrated through simulation results and experimental implementation.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.1-7
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• 2014

In this paper, we propose a virtual machine(VM) remapping scheme that reduces VM relocation time on a cloud cluster. The proposed scheme finds VMs that should be migrated in sequence from a given VM map, and exchanges destinations of some VMs among them to reduce the VM relocation time. The VMs, the destinations of which will be exchanged, are chosen based on the amount of physical machine's available resources and migration completion time. The exchange of destinations is repeated until the VM relocation time cannot be shortened any further. Through a simulation, we show that the proposed scheme reduces VM relocation time by 42.7% in maximum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5234-5251
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• 2018

It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• International journal of advanced smart convergence
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• v.2 no.2
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• pp.7-9
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• 2013

Current physics-based simulation is an important tool in the fluid animation. However some problems require a new change to current research trends which depend only on the simulation. The ultimate goal of this project is to obtain information of flow example, analyze an example through machine learning and the novel fluid animation reconfigure without physical simulation.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1259-1265
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• 2004

Electro-magnetic clutch which is affected by elements of electric, magnetic and mechanical friction is important that it has a characteristic changes by environmental impact. Expecially, it is equipped in railroad sideway which is bleak external environment. Therefore, this paper investigated affections by temperature changes, which can make out through physical characteristics such as friction torque, minimum slipping torque, rated slipping torque of electro-magnetic dutch.

• STI Policy Review
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• v.9 no.1
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• pp.133-157
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• 2018

The divide between the "digital" economy and the traditional "physical" economy is outdated. In fact, we are in a transition to a singular economy. This paper classifies economic objects (including actors) as either physical or virtual and argues that due to emerging technologies, these objects are interacting with each other in both physical and increasingly digital spheres in tandem. This paper recognizes the elemental difference between atoms and bytes but argues that physical and digital economic activities are becoming inseparably intertwined. Furthermore, arbitrarily dividing the economy into two categories - one "physical" and the other "digital" - distorts the overall view of the actual execution of economic activity. A wide range of innovations emerging concurrently is fueling the transition to a singular economy. Often referred to as the elements of the Fourth Industrial Revolution (4IR), four emerging technological areas are reviewed here: distributed ledger technology, artificial intelligence/machine learning/data sciences, biometrics and remote sensor technologies, and access infrastructure (universal internet access/electricity/cloud computing). The financial services sector is presented as a case study for the potential impact of these 4IR technologies and the blurring physical/digital line. To reach the potential of these innovations and a truly singular economy, it requires the concurrent development of social, organizational, and regulatory innovations, though they lag in terms of technological progress thus far.

• Journal of Biomedical Engineering Research
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• v.33 no.4
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• pp.184-193
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• 2012

Many studies have reported that housework done using household appliances may affect biomechanical characteristics of the musculoskeletal system. The purpose of this study was to investigate the effects of housework done using a pulsator washing machine on joints and muscles. We calculated joint torques and muscle strengths on the basis of an experimental/virtual three-dimensional motion analysis for six healthy females using the pulsator washing machine at different heights (H) and depths (D) of the inside drum (H (mm){\times}D (mm), Trial1: $962.5{\times}609.4$, Trial2: $962.5{\times}624.4$, Trial3: $982.5{\times}644.4$, Trial4: $995.5{\times}642.4$, Trial5: $1015.5{\times}677.4$). The joint torques and muscle strengths tended to be considerably different during torso flexion in the sagittal plane for all the trials. The maximum joint torques for the thoracic vertebra, left ankle, and right shoulder measured in Trial4 were significantly higher than those measured in the other trials (p < 0.05); in addition, those for the thoracic vertebra and both ankles measured in Trial5 were significantly higher than those measured in the other trials (p < 0.05). The maximum muscle strengths for the left trapezius muscle and both tibialis anterior muscles measured in Trial5 were significantly higher than those measured in the other trials (p < 0.05). These results indicate that housework done using a pulsator washing machine may affect joint torques and muscle strengths, and these effects are dependent on the height and/or depth of the inside drum of the pulsator washing machine.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1648-1652
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• 2004

An advanced training machine designed for elderly people is proposed. The training machine allows users to have a safe and effective training through exercise close to ordinal motion appears in daily life such as standing up/down motion. The activation level of user's muscle is real timely monitored during the exercise and the training load is adjusted based on the body information. The training load is exerted and continuously controlled by actuation of an air cylinder.