• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.11 no.2
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• pp.31-39
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• 2015

Pipe wall-thinning by flow-accelerated corrosion (FAC) and various types of erosion is a significant and costly damage phenomenon in secondary piping systems of nuclear power plants (NPPs). Most NPPs have management programs to ensure pipe integrity due to wall-thinning that includes periodic measurements for pipe wall thicknesses using ultrasonic tests (UTs). Nevertheless, thinning evaluations are not easy because the amount of thickness reduction being measured is often quite small compared to the accuracy of the inspection technique. U.S. Electric Power Research Institute (EPRI) had proposed Total Point Method (TPM) as a thinning occurrence evaluation method, which is a very useful method for detecting locally thinned pipes or fittings. However, evaluation engineers have to discern manually the measurement data because there are no numerical algorithm for TPM. In this study, numerical algorithms were developed based on non-parametric and parametric statistical method.

• Journal of Biomedical Engineering Research
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• v.35 no.1
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• pp.8-13
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• 2014

In this paper, the gait imbalance evaluation algorithm based on temporal symmetry ratio using encoder is proposed. The device is attached to the hip joint in order to measure the angle during the normal gait. Using an angle data, the stance phase and swing phase was determined. And the value of TSR(temporal symmetry ratio) was calculated by stance phase and swing phase of gait cycle. For the comparative experiment, the conventional method of the foot pressure was measured at the same conditions. The results of statistical analysis, there was a significant difference (p < 0.05) when using encoder. The gait imbalance analysis using encoder is effective in determining the imbalance than using the existing method of pressure.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.155-160
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• 2023

This paper presents a method for predicting blood pressure using the photoplethysmography signals. First, after measuring the optical blood flow signal, artifacts are removed through a preprocessing process, and a signal for learning is obtained. In addition, weight and height, which affect blood pressure, are measured as additional information. Next, a system is built to estimate systolic and diastolic blood pressure by learning the photoplethysmography signals, height, and weight as input variables through an artificial intelligence algorithm. The constructed system predicts the systolic and diastolic blood pressures using the inputs. The proposed method can continuously predict blood pressure in real time by receiving photoplethysmography signals that reflect the state of the heart and blood vessels, and the height and weight of the subject in an unconstrained method. In order to confirm the usefulness of the artificial intelligence-based blood pressure prediction system presented in this study, the usefulness of the results is verified by comparing the measured blood pressure with the predicted blood pressure.

• Journal of Mechanical Science and Technology
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• v.14 no.1
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• pp.93-102
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• 2000

The objective of the present study is to investigate the pressure-strain correlation terms of the Reynolds stress models for the three dimensional turbulent boundary layer in a $30^{\circ}$ bend tunnel. The numerical results obtained by models of Launder, Reece and Rodi (LRR) , Fu and Speziale, Sarkar and Gatski (SSG) for the pressure-strain correlation terms are compared against experimental data and the calculated results from the standard k-${\varepsilon}$ model. The governing equations are discretized by the finite volume method and SIMPLE algorithm is used to calculate the pressure field. The results show that the models of LRR and SSG predict the anisotropy of turbulent structure better than the standard k-${\varepsilon}$ model. Also, the results obtained from the LRR and SSG models are in better agreement with the experimental data than those of the Fu and standard k-${\varepsilon}$ models with regard to turbulent normal stresses. Nevertheless, LRR and SSG models do not effectively predict pressure-strain redistribution terms in the inner layer because the pressure-strain terms are based on the locally homogeneous approximation. Therefore, to give better predictions of the pressure-strain terms, non-local effects should be considered.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2361-2370
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• 2000

The introduction of inexpensive cylinder pressure sensors provides new opportunities for precise engine control. This paper presents a control strategy of spark advance based upon cylinder pressure of spark ignition engines. A location of peak pressure(LPP) is the major parameter for controlling the spark timing, and also the UP is estimated, using a multi-layer feedforward neural network, which needs only five pressure sensor output voltage samples at -40˚, -20˚, 0˚, 20˚, 40˚ after top dead center. The neural network plays an important role in mitigating the A/D conversion load of an electronic engine controller by increasing the sampling interval from 10 crank angle(CA) to 20˚ CA. A proposed control algorithm does not need a sensor calibration and pegging(bias calculation) procedure because the neural network estimates the UP from the raw sensor output voltage. The estimated LPP can be regarded as a good index for combustion phasing, and can also be used as an MBT control parameter. The feasibility of this methodology is closely examined through steady and transient engine operations to control individual cylinder spark advance. The experimental results have revealed a favorable agreement of individual cylinder optimal combustion phasing.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.3
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• pp.21-29
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• 2013

Turbulent flow and thermal fields of gaseous hydrogen/liquid oxygen flames at supercritical pressure are investigated by turbulence models. The modified Soave-Redlich-Kwong (SRK) EOS is implemented into the flamelet model to realize real-fluid combustions. For supercritical fluid flows, the modified pressure-velocity-density coupling are introduced. Based on the algorithm, the relative performance of six convection schemes and the predictions of four turbulence models are compared. The selected turbulence models are needed to be modified to consider various characteristics of real-fluid combustions.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.378-384
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• 2014

Replication routing can greatly improve the data delivery performance by enabling multiple replicas of the same packet to be transmitted towards its destination simultaneously. It has been studied extensively recently and is now a widely accepted routing paradigm in delay tolerant networks (DTNs). However, in this field, the issue of how to maximize the utilization efficiency of limited replication quota in a resource-saving manner and therefore making replication routing to be more efficient in networks with limited resources has not received enough attention. In this paper, we propose a DTN routing protocol with back-pressure based replica distribution. Our protocol models the replica distribution problem from a resource allocation perspective and it utilizes the idea of back-pressure algorithm, which can be used for providing efficient network resource allocation for replication quota assignment among encountered nodes. Simulation results demonstrate that the proposed protocol significantly outperforms existing replication routing protocols in terms of packet delay and delivery ratio.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.413-414
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• 2008

Abstract should be in English. The leaning angle optimization of turbine blade using the genetic algorithm was conducted in this paper. The calculation CFD technique was based upon the Diagonalized Alternating Directional Implicit scheme(DADI) with algebraic turbulencemodeling. The leaning angle of VKI turbine blade was represented using B-spline curve. The control points are the design variable. Genetic algorithm was taken into account as an optimization tool. The objective was to minimize the total pressure loss. The optimized final geometry shows the better aerodynamic performance compared with the initial turbine blade.

• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.