• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.305-312
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• 2023

Physics-Informed Neural Network (PINN) is used to invert bubble size distributions from attenuation losses. By considering a linear system for the bubble population inversion, Adaptive Learned Iterative Shrinkage Thresholding Algorithm (Ada-LISTA), which has been solved linear systems in image processing, is used as a neural network architecture in PINN. Furthermore, a regularization based on the linear system is added to a loss function of PINN and it makes a PINN have better generalization by a solution satisfying the bubble physics. To evaluate an uncertainty of bubble estimation, deep ensemble is adopted. 20 Ada-LISTAs with different initial values are trained using the same training dataset. During test with attenuation losses different from those in the training dataset, the bubble size distribution and corresponding uncertainty are indicated by average and variance of 20 estimations, respectively. Deep ensemble Ada-LISTA demonstrate superior performance in inverting bubble size distributions than the conventional convex optimization solver of CVX.

• Electrical & Electronic Materials
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• v.10 no.3
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• pp.268-273
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• 1997

In this paper the transient electron transport in GaAs bulk is simulated by using ensemble Monte Carlo method. To analyze the transient electron transport the 10000 electrons in the .GAMMA. valley are simulated simultaneously for 10 picoseconds. The electric field-velocity relation is obtained. The high impurity density reduces the negative differential resistance effect. The result of transient average velocity shows the electron velocity in the transient state is faster than that in the steady state. This transient velocity overshoot is caused by the intervalley scattering mechanism. And we confirmed the fact that the energy relaxation time is longer than the momentum relaxation time.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.93-95
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• 1991

As the ensemble averaged dZ/dt signal during exercise is smoothed, it is difficult to find the distinctive marks. The cross correlation function was made use of estmating these marks. LVET was calculated based on the calculated parameters of the characteristic points. For the accuracy validation, LVET calculated by hand, by the ensemble average and the cross correl at ion were compared.

• Electrical & Electronic Materials
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• v.10 no.10
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• pp.1000-1004
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• 1997

In this paper the current-voltage characteristics of a submicron GaAs MESFET is simulated by using the self-consistent ensemble Monte Carlo method. The numerical algorithm employed in solving the two-dimensional Poisson equation is the successive over-relaxation(SOR) method. The total number of employed superparticles is about 1000 and the field adjusting time is 10fs. To obtain the steady-state results the simulation is performed for 10ps at each bias condition. The simulation results show the average electron velocity is modified by the gate voltage.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.2
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• pp.31-43
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• 2000

This paper presents a novel algorithm lot extraction of the eye and mouth fields (facial features) from 2D gray level face images. First of all, it has been found that Eigenfeatures, derived from the eigenvalues and the eigenvectors of the binary edge data set constructed from the eye and mouth fields are very good features to locate these fields. The Eigenfeatures, extracted from the positive and negative training samples for the facial features, ate used to train a MultiLayer Perceptron(MLP) whose output indicates the degree to which a particular image window contains the eye or the mouth within itself. Second, to ensure robustness, the ensemble network consisting of multiple MLPs is used instead of a single MLP. The output of the ensemble network becomes the average of the multiple locations of the field each found by the constituent MLPs. Finally, in order to reduce the computation time, we extracted the coarse search region lot eyes and mouth by using prior information on face images. The advantages of the proposed approach includes that only a small number of frontal faces are sufficient to train the nets and furthermore, lends themselves to good generalization to non-frontal poses and even to other people's faces. It was also experimentally verified that the proposed algorithm is robust against slight variations of facial size and pose due to the generalization characteristics of neural networks.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.455-463
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• 2003

Time series of suspended sediment concentration, surface elevation and velocity were measured and analysed to investigate the role of waves and the predominance of infra-gravity wave component for sediment suspension phenomena in the surf zone. For the investigation in detail, we adopted the cross spectral analysis method between suspended sediment concentration and the characteristic values of wave, and ensemble average analysis method about long-period wave component, which is dominant to sediment suspension in the measurement point. The obtained results are summarized as follows: 1)The relationship between suspended sediment concentration and the characteristic values of wave is stronger for the long-period standing wave components(about 60s and 30s where the nodal point of the first mode and the anti-nodal point of the second mode are located at the measurement point, respectively) than the long wave components(about 100s), which have the most energetic power, 2) and also, it is cleared that suspended sediment concentration is increased in the case of the phase, the velocity components of the first mode long-period standing wave(60sec) were accelerated toward on-shore direction, that is, the water surface in offshore side is higher than on-shore side.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.187-198
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• 2006

Korea Water Resources Corporation(KOWACO) has developed the Integrated Real-time Water Management System(IRWMS) that calculates monthly optimal ending target storages by using Sampling Stochastic Dynamic Programming(SSDP) with Ensemble Streamflow Prediction(ESP) running on the $1^{st}$ day of each month. This system, however, has a shortcoming: it cannot reflect the hydrolmeteorologic variations in the middle of the month. To overcome this drawback, in this study updated ESP forecasts three times each month by using the observed precipitation series from the $1^{st}$ day of the month to the forecast day and the historical precipitation ensemble for the remaining days. The improved accuracy and its effect on the reservoir operations were quantified as a result. SSDP/ESP21 that reflects within-a-month hydrolmeteorologic states saves $1\;X\;10^6\;m^3$ in water shortage on average than SSDP/ESP01. In addition, the simulation result demonstrated that the effect of ESP accuracy on the reduction of water shortage became more important when the total runoff was low during the drawdown period.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.273-278
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• 2002

The characteristics of tip vortex within a blade tip region were examined experimentally in various flow coefficients by the way of changing tip clearance and blade stagger angle in an axial Low Speed Research Compressor(LSRC). The objective was to identify the unsteady pressure distribution in the blade passage by ensemble average technique acquired from high-frequency response pressure transducers and the tip vortex by root mean square value(RMS value). Data were reduced statistically using phase-lock technique for detailed pressure distributions.