• 제목/Summary/Keyword: ensembles

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Tuning of the Interparticle interactions in ultrafine ferrihydrite nanoparticles

  • Knyazev, Yuriy V.;Balaev, Dmitry A.;Yaroslavtsev, Roman N.;Krasikov, Aleksandr A.;Velikanov, Dmitry A.;Mikhlin, Yuriy L.;Volochaev, Mikhail N.;Bayukov, Oleg A.;Stolyar, Sergei V.;Iskhakov, Rauf S.
    • Advances in nano research
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    • v.12 no.6
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    • pp.605-616
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    • 2022
  • We prepared two samples of ultrafine ferrihydrite (FH) nanoparticle ensembles of quite a different origin. First is the biosynthesized sample (as a product of the vital activity of bacteria Klebsiella oxytoca (hereinafter marked as FH-bact) with a natural organic coating and negligible magnetic interparticle interactions. And the second one is the chemically synthesized ferrihydrite (hereinafter FH-chem) without any coating and high level of the interparticle interactions. The interparticle magnetic interactions have been tuned by modifying the nanoparticle surface in both samples. The coating of the FH-bact sample has been partially removed by annealing at 150℃ for 24 h (hereinafter FH-annealed). The FH-chem sample, vice versa, has been coated (1.0 g) with biocompatible polysaccharide (arabinogalactan) in an ultrasonic bath for 10 min (hereinafter FH-coated). The changes in the surface properties of nanoparticles have been controlled by XPS. According to the electron microscopy data, the modification of the nanoparticle surface does not drastically change the particle shape and size. A change in the average nanoparticle size in sample FH-annealed to 3.3 nm relative to the value in the other samples (2.6 nm) has only been observed. The estimated particle coating thickness is about 0.2-0.3 nm for samples FH-bact and FH-coated and 0.1 nm for sample FH-annealed. Mössbauer and magnetization measurements are definitely shown that the drastic change in the blocking temperature is caused by the interparticle interactions. The experimental temperature dependences of the hyperfine field hf>(T) for samples FH-bact and FH-coated have not revealed the effect of interparticle interactions. Otherwise, the interparticle interaction energy Eint estimated from the hf>(T) for samples FH-chem and FH-annealed has been found to be 121kB and 259kB, respectively.

Application of a large-scale ensemble climate simulation database for estimating the extreme rainfall (극한강우량 산정을 위한 대규모 기후 앙상블 모의자료의 적용)

  • Kim, Youngkyu;Son, Minwoo
    • Journal of Korea Water Resources Association
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    • v.55 no.3
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    • pp.177-189
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    • 2022
  • The purpose of this study is to apply the d4PDF (Data for Policy Decision Making for Future Change) constructed from a large-scale ensemble climate simulation to estimate the probable rainfall with low frequency and high intensity. In addition, this study analyzes the uncertainty caused by the application of the frequency analysis by comparing the probable rainfall estimated using the d4PDF with that estimated using the observed data and frequency analysis at Geunsam, Imsil, Jeonju, and Jangsu stations. The d4PDF data consists of a total of 50 ensembles, and one ensemble provides climate and weather data for 60 years such as rainfall and temperature. Thus, it was possible to collect 3,000 annual maximum daily rainfall for each station. By using these characteristics, this study does not apply the frequency analysis for estimating the probability rainfall, and we estimated the probability rainfall with a return period of 10 to 1000 years by distributing 3,000 rainfall by the magnitude based on a non-parametric approach. Then, the estimated probability rainfall using d4PDF was compared with those estimated using the Gumbel or GEV distribution and the observed rainfall, and the deviation between two probability rainfall was estimated. As a result, this deviation increased as the difference between the return period and the observation period increased. Meanwhile, the d4PDF reasonably suggested the probability rainfall with a low frequency and high intensity by minimizing the uncertainty occurred by applying the frequency analysis and the observed data with the short data period.

A Study on the Image Change Using Twinkle Artifact Images and Phantom according to Calcification-Inducing Environment in Breast Ultrasonography (유방 초음파 검사에서 석회화 유발 환경에 따른 반짝 허상과 팸텀을 활용한 영상 변화에 관한 연구)

  • Cheol-Min Jeon
    • Journal of the Korean Society of Radiology
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    • v.17 no.5
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    • pp.751-759
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    • 2023
  • Breast ultrasonography is difficult to image in fatty breasts and to find micro-calcification, but the discovery of micro-calcification is very important for breast cancer screening. Among the color Doppler artifact of ultrasound, twinkle artifact mainly occur on strong reflectors such as stones or calcification in images, and evaluation methods using them are clinically being used. In this study, we are conducting experiments on the color Doppler settings of ultrasound equipment, such as repetition frequency, ensemble, persist, wall filtering, smoothing, linear density, and dissociation value, by producing a breast simulation phantom using the largest amount of calcium phosphate among breast implants. The purpose of this study was to improve the contrast of twinkle artifact in breast ultrasound examinations and to maximize their use in clinical practice. As a result, the pulse repetition frequency occurred in the range of 3.6 kHz to 7.2 kHz, and did not occur above 10.5 kHz. For ensembles, twinkle artifact occurred in all sizes of calcification under low conditions, and in threshold settings, the twinkle artifact increased slightly only under 80 to 100 conditions, and did not occur in 1 mm size calcification. Persist, wall filter, smoothing, and line density settings did not have much meaning in the setting variable because conditions did not increase by condition, and pulse repetition frequency, ensemble, and thresholds had the greatest impact on the twinkling artifact image. This study is expected to help examiners select optimal conditions to effectively increase twinkle artifact by adjusting color Doppler settings.

Evaluation of Agro-Climatic Index Using Multi-Model Ensemble Downscaled Climate Prediction of CMIP5 (상세화된 CMIP5 기후변화전망의 다중모델앙상블 접근에 의한 농업기후지수 평가)

  • Chung, Uran;Cho, Jaepil;Lee, Eun-Jeong
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.17 no.2
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    • pp.108-125
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    • 2015
  • The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

Optimal Selection of Classifier Ensemble Using Genetic Algorithms (유전자 알고리즘을 이용한 분류자 앙상블의 최적 선택)

  • Kim, Myung-Jong
    • Journal of Intelligence and Information Systems
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    • v.16 no.4
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    • pp.99-112
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    • 2010
  • Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological Mean Bias Characteristics (기상청 기후예측시스템(GloSea6) - Part 2: 기후모의 평균 오차 특성 분석)

  • Hyun, Yu-Kyung;Lee, Johan;Shin, Beomcheol;Choi, Yuna;Kim, Ji-Yeong;Lee, Sang-Min;Ji, Hee-Sook;Boo, Kyung-On;Lim, Somin;Kim, Hyeri;Ryu, Young;Park, Yeon-Hee;Park, Hyeong-Sik;Choo, Sung-Ho;Hyun, Seung-Hwon;Hwang, Seung-On
    • Atmosphere
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    • v.32 no.2
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    • pp.87-101
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    • 2022
  • In this paper, the performance improvement for the new KMA's Climate Prediction System (GloSea6), which has been built and tested in 2021, is presented by assessing the bias distribution of basic variables from 24 years of GloSea6 hindcasts. Along with the upgrade from GloSea5 to GloSea6, the performance of GloSea6 can be regarded as notable in many respects: improvements in (i) negative bias of geopotential height over the tropical and mid-latitude troposphere and over polar stratosphere in boreal summer; (ii) cold bias of tropospheric temperature; (iii) underestimation of mid-latitude jets; (iv) dry bias in the lower troposphere; (v) cold tongue bias in the equatorial SST and the warm bias of Southern Ocean, suggesting the potential of improvements to the major climate variability in GloSea6. The warm surface temperature in the northern hemisphere continent in summer is eliminated by using CDF-matched soil-moisture initials. However, the cold bias in high latitude snow-covered area in winter still needs to be improved in the future. The intensification of the westerly winds of the summer Asian monsoon and the weakening of the northwest Pacific high, which are considered to be major errors in the GloSea system, had not been significantly improved. However, both the use of increased number of ensembles and the initial conditions at the closest initial dates reveals possibility to improve these biases. It is also noted that the effect of ensemble expansion mainly contributes to the improvement of annual variability over high latitudes and polar regions.