• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.17-23
• /
• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.57-65
• /
• 2007

This paper presents the design of single phase PFC(Power Factor Correction) using a DSP(TMS320F2812). In order to realize the proposed boost PFC converter in average current mode control, the DSP requires the A/D sampling values for a line input voltage, a inductor current, and the output voltage of the converter. Because of a FET switching noise, these sampling values contain a high frequency noise and switching ripple. The solution of A/D sampling keeps away from the switching point. Because the PWM duty is changed from 5% to 95%, we can#t decide a fixed sampling time. In this paper, the three A/D converters of the DSP are started using the prediction algorithm for the FET ON/OFF time at every sampling cycle(40 KHz). Implemented A/D sampling algorithm with only one timer of the DSP is very simple and gives the autostart of these A/D converters. From the experimental result, it was shown that the power factor was about 0.99 at wide input voltage, and the output ripple voltage was smaller than 5 Vpp at 80 Vdc output. Finally the parameters and gains of PI controllers are controlled by serial communication with Windows Xp based PC. Also it was shown that the implemented PFC converter can achieve the feasibility and the usefulness.

• Atmosphere
• /
• v.24 no.1
• /
• pp.123-130
• /
• 2014

The automated prediction system for seasonal tropical cyclone (TC) activity is established at the National Typhoon Center of the Korea Meteorological Administration (KMA) to provide effective operation and control of the system for user who lacks knowledge of the system. For automation of the system, two procedures which include subjective decisions by user are performed in advance, and their output data are provided as input data. To provide the capability to understand the operational processes for operational user, the input and output data are summarized with each process, and the directory structure is reconstructed following KMA's standard. We introduce a user interface using namelist input parameters to effectively control operational conditions which is fixed or should be manually set in the previous version of the prediction system. To operationally use early prediction which become available through the automation, its performances are evaluated according to initial condition dates. As a result, high correlations between the observed and predicted TC counts are kept for all track clusters even though advancing the initial condition date from May to January.

• Structural Engineering and Mechanics
• /
• v.67 no.2
• /
• pp.105-113
• /
• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• Korean Journal of Clinical Pharmacy
• /
• v.16 no.1
• /
• pp.63-68
• /
• 2006

Gabapentin, 1-(aminomethyl-1-cyclohexyl)acetic acid, is anew antiepileptic drug related to ${\gamma}-aminobutyric$ acid(GABA) currently being introduced in therapy worldwide. The bioavailability and pharmacokinetics of gabapentin capsules were examined in 22 volunteers who received a single oral dose in the fasting state by randomized balanced $2{\times}2$ crossover design. After dosing, blood samples were collected for a period of 24 hours and analyzed by liquid chromatography-tandem mass spectrometry (LC/MS/MS). Time course of plasma gabapentin concentration was analyzed with non-compartmental and compartmental approaches. $WinNonlin^{(R)}$, the kinetic computer program, was used for compartmental analysis. One compartment model with first-order input, first-order output with no lag time and weighting by $1/(predieted\;y)^2$ was chosen as the most appropriate pharmacokinetic model for the volunteers. The major pharmacokinetic parameters $(AUC_{0-24hr},\;AUC_{inf},\;C_{max}\;and\;T_{max})$ and other parameters $(K_a,\;K_{el},\;V_d/F\;and\;Cl/F)$ of $Gapentin^{TM}$ (test drug) and $Neurontin^{TM}$ (reference drug) were estimated by non-compartmental analysis and compartmental analysis. The 90% confidence intervals of mean difference of logarithmic transformed $AUC_{0-24hr}\;and\;C_{max}$ were $log(0.9106){\sim}log(1.l254)\;and\;log(0.8521){\sim}log(1.0505)$, respectively. It shows that the bioavailability of the test drug is equivalent with that of the reference drug. There was no statistically significant difference between the two drugs in all pharmacokinetic parameters.

• Journal of the Korean Society of Radiology
• /
• v.17 no.5
• /
• pp.709-715
• /
• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Journal of Korean Port Research
• /
• v.15 no.1
• /
• pp.37-46
• /
• 2001

Fuzzy logic control(FLC) is composed of three parts : fuzzy rule-bases, membership functions, and scaling factors. Well-defined fuzzy rule-base should contain proper physical intuition on the plant, so are needed lots of experiences of the skillful expert. When membership functions are considered, some parameters on the memberships function such as function shape, support, allocation density should be selected well. The rule of scaling factors is 'scaling'(amplifying or reducing) for both input and output signals of the FLC to fit in the membership function support and to operate the plant intentionally. To get a better performance of the FLC, it is necessary to adjust the parameters of the FLC. In general, the adaptation of the scaling factors is the most effective adjustment scheme, compared with that of the fuzzy rule-base or membership function parameters. This study proposes the adaptation scheme of the scaling factors. When the adaptation is performed on-line, the stability of the adaptive FLC should be guaranteed. The stable FLC system can be designed with stability analysis in the sense of Lyapunov stability. To adapt the scaling factors for the error signals, the concept of the conventional MRAC would be introduced into slightly modified form. A tracking accuracy of the control system would be enhanced by the modified shape and support of the membership function. The simulation is achieved on the pilot plant with the hydraulic steering control of a UCT(Unmanned Container Transporter) of which modeling dynamics have lots of severe uncertainties and modeling errors.

• Composite Materials and Engineering
• /
• v.3 no.3
• /
• pp.201-220
• /
• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• YAKHAK HOEJI
• /
• v.42 no.5
• /
• pp.513-518
• /
• 1998

Fluoxetine is a nontricyclic antidepressant which blocks serotonin reuptake selectively. Its N-demethyl metabolite, norfluoxetine is also selective inhibitor of serotonin uptake . This study was carried out to compare the bioavailability of Myung-in fluoxetine (20mg/cap.) with that of Prozac$^{\circde{R}}$. The bioavailability was conducted on 24 healthy volunteers who received a single dose (80mg) of each drug in the fasting state, in a randomized balanced 2-way crossover design. After closing, serial blood samples were collected for a period of 48 hours, Plasma was analyzed for fluoxetine and norfluoxetine by a sensitive and validated HPLC assay. The major pharmacokinetic parameters ($AUC_{0-48\;hr}$, Cmax, Tmax , $AUC_{inf.}$, MRT. $T_{1/2}$, Vd and Cl) were, calculated from the plasma fluoxetine concentration-time data of each volunteer. The microcomputer program, 'WinNonlin' was used for compartmental analysis. A two-compartment model with first-order input, first-order output and no lag time was chosen as the most appropriate pharmacokinetic model. The data were best described by using a weighting factor of $1/y^2$. Though the plasma fluoxetine concentrations of Myung-in fluoxetine were higher than those of Prozac$^{\circde{R}}$ at all observed time from 7.9% to 16.9% (P<0.05 at 6.7 and 10 hr), the bioavailability of Myung-in fluoxetine appeared to be bioequivalent with that of Prozac$^{\circde{R}}$. There were no statistical significant differences between the two drugs in all pharmacokinetic parameters including $AUC_{0-48\;hr}$ of norfluoxetine.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.05a
• /
• pp.1334-1337
• /
• 2006

System identification is the field of modeling dynamic systems from experimental data. As a modeling technique, we can mention finite element method (FEM). In addition, we are able to measure modal data as the experimental data. The system can be generally categorized into a gray box and black box. In the gray box, we know mathematical model of a system, but we don't know structural parameters exactly, so we need to estimate structural parameters. In the black box, we don't know a system completely, so we need to identify system from nothing. To date, various system identification methods have been developed. Among them, we introduce system realization theory which uses Hankel matrix and Eigensystem Realization Algorithm (ERA) that enable us to identify modal parameters from noisy measurement data. Although we obtain noise-free data, however, we are likely to face difficulties in identifying a structure with hidden modes. Hidden modes can be occurred when the input or output position comes to a nodal point. If we change a system using a mode decoupling controller, the hidden modes can be revealed. Because we know the perturbation quantities in a closed loop system with the controller, we can realize an original system by subtracting perturbation quantities from the closed loop system. In this paper, we propose a novel method to identify a structure with hidden modes using the mode decoupling controller and the associated example is given for illustration.