288,470 research outputs found
Kim Knuckey
No full text"[SX 25521] Kim Knuckey Don/R - Sigs Alice Springs 1941/42".[SX 25521] Kim Knuckey. Despatch Rider - Signals, Alice Springs, 1941/42
CVTresh: R Package for Level-Dependent Cross-Validation Thresholding
No full textThe core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional cross-validation with a fast imputation method, so that it overcomes a limitation of data length, a power of 2. It can be easily applied to the classical leave-one-out cross-validation and K-fold cross-validation. Since the procedure is computationally fast, a level-dependent cross-validation can be developed for wavelet shrinkage of data with various sparseness according to levels.
kim: Behavioral Scientists' Analysis Toolkit (an R package)
No full textThis R package contains various functions that simplify and expedite analyses of experimental data.
Examples include a function that plots sample means of groups in a factorial experimental design, a function that conducts robust regressions with bootstrapped samples, and a function that conducts robust two-way analysis of variance.
To use this package, please follow the steps below.
1. In R, install the package from CRAN by typing:
install.packages("kim")
2. After the package is installed, attach the package in R by typing:
library(kim)
3. After the package is attached, update the package to the newest version from the package's Github page by typing:
update_kim(
kim: Functions for Behavioral Science Researchers (an R package)
No full textThis R package contains various functions that simplify and expedite analyses of experimental data.
Examples include a function that plots sample means of groups in a factorial experimental design, a function that conducts robust regressions with bootstrapped samples, and a function that conducts robust two-way analysis of variance.
To use this package, please follow the steps below.
1. In R, install the package from CRAN by typing:
install.packages("kim")
2. After the package is installed, attach the package in R by typing:
library(kim)
3. After the package is attached, update the package to the newest version from the package's Github page by typing:
update_kim(
DBLP-derived labeled data for author name disambiguation
No full textThis is a DBLP-derived labeled data originally created by Dr. C. Lee Giles at Penn State University and filtered for duplicate removal and error correction by Dr. Jinseok Kim at University of Michigan. For more details, see references below.1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9Each row refers to an author name instance with following feature information separated by tab.author name: full name string extracted from DBLPunique author id: labels assigned manually by Dr. C. Lee Giles's teampaper id: assigned by Dr. Jinseok Kimauthor list: names of authors in the byline of the paperyear: publication yearvenue: conference or journal namestitle: stopwords removed and stemmed by the Porter's stemmerIf you want to use this dataset, please consider to cite papers below.For the original dataset: Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two Supervised Learning Approaches for Name Disambiguation in Author Citations. JCDL 2004: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries, 296-305. doi:10.1145/996350.996419For the filtered dataset: 1. Kim, Jinseok (2018). Evaluating author name disambiguation for digital libraries: a case of DBLP. Scientometrics. doi:10.1007/s11192-018-2824-5 or2. Kim, Jinseok & Kim, Jenna (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics. doi: 10.1007/s11192-018-2865-9</div
Author Correction: Evaluation of skin cancer resection guide using hyper‑realistic in‑vitro phantom fabricated by 3D printing
No full textThe original version of this Article contained an error in the spelling of the author Taehun Kim which was incorrectly given as Teahun Kim. The original Article has been corrected
Bio-vison 2016: The second national framework plan for biotechnology promotion in Korea
No full textThis research was funded by the Specific Research
and Development Project of the Ministry of Science
and Technology.This material is based on Bio-Vision
2016 and its Report [5, 6].We are very grateful to the
following members of the Biotechnology Policy Research
Center: Young-Cheol Kim, Dong-Sub Yoon,
Moo Woong Kim, Eun Jung Kim, Su Gil Kim, Mi jeong
Park, Seong-Hoon Park, Oh-Min Joung, and Seung-
Hoo Shin
Efficient on-chip decoupling capacitor design on an 8-bit microcontroller to reduce simultaneous switching noise and electromagnetic radiated emission
No full textWe have thoroughly investigated the effect of on-chip decoupling capacitors on the simultaneous switching noise (SSN) and the radiated emission. Furthermore, we have successfully demonstrated an efficient design method for on-chip decoupling capacitors on an 8-bit microcontroller without increasing the die size, which results in more than 10 dB of suppressed radiated emission.The authors are grateful to Young-hwan Yun, Seog-heon Ham, Yong-hee Lee and Do-won Kim of Samsung Electronics, who provided the test IC chip layout and fabrication. We would like to thank D.K.Han, Y.S.Park, H.J.Yoon and M.K.Oh of Hynix Semiconductor for the fabrication of the controller IC chip being tested. We also thank H.W.Shim of ETRI-Korea for giving assistance with the EMI measurement, and Young-dae Kim of TESCOM who provided the TEM cell
DEVELOPMENT OF SYNTHETIC SPECIMENS FOR CALIBRATION AND EVALUATION OF M SUB R (RESILIENT MODULUS) EQUIPMENT
No full textLaboratory measurement of the deformational characteristics of subgrade materials can be quite difficult because of the small values of stress and strain typically involved and the need to eliminate equipment compliance. Measurement of resilient modulus (M sub R) of subgrades falls into this category. Therefore, synthetic specimens with known stiffness characteristics would be beneficial in evaluating and calibrating M sub R equipment as well as training personnel. Two-component urethane elastomer resins are shown to make good candidates for calibration specimens. They can be made with a wide range of stiffnesses that vary from soft subgrades to stiff uncemented bases. Urethane can be modeled as a linear, viscoelastic material with stiffness characteristics essentially independent of confining pressure, strain amplitude, and stress history for the type of cyclic loading used in M sub R testing. Urethane stiffness is, however, dependent on loading frequency and temperature. Therefore, values of Youngs modulus used to equate to M sub R have to be selected at the appropriate frequency and temperature
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