• Journal of Ecology and Environment
• /
• v.35 no.3
• /
• pp.189-194
• /
• 2012

Coarse woody debris (CWD) is generally considered dead woody material in various stages of forest decomposition and has been hypothesized to be an important habitat feature for mammals in forests of the Pacific Northwest, USA. Sherman and pitfall trapping were conducted for 2 years on three paired sites with low and high amounts of CWD. Deer mice was the dominant species with a total capture of 605 (45.6%). Four species of insectivores were captured, including Sorex moncicolus, S. trowbridgii, S. vagrans, and Neurotrichus gibbsii. A Poisson regression model was used to test whether 11 CWD variables could predict insectivore captures. The volume of logs and mean decay were important variables for deer mice use of CWD. Mean distance from pieces of CWD to the capture point was significantly related to the total number of captures of trowbridge shrew (Sorex trowbridgii) and all insectivore species. Vagrant shrews (Sorex vagrans) were significantly associated with log volume. Retaining large size CWD should be part of a management plan for ground-dwelling insectivores in forests to secure their biodiversity.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.15 no.3
• /
• pp.39-44
• /
• 2006

In this study, we nodestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of welding flasw. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from welding flaws in time domain. Through this process, we confirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• Environmental Engineering Research
• /
• v.14 no.2
• /
• pp.71-78
• /
• 2009

A review of stormwater quantity and quality in the urban environment is presented. The review is presented in three parts. The first part reviews the mathematical methods for stormwater quantity and has been undertaken by examining a number of stormwater models that are in current use. The important feature of models, their applications, and management has been discussed. Different types of stormwater management models are presented in the literatures. Generally, all the models are simplified as conceptual or empirical depending on whether the model is based on physical laws or not. In both cases if any of the variables in the model are regarded as random variables having a probability distribution, then the model is stochastic model. Otherwise the model is deterministic (based on process descriptions). The analytical techniques are presented in this paper.

• Transactions of Materials Processing
• /
• v.13 no.2
• /
• pp.160-167
• /
• 2004

In order to theoretically analyze the creep behavior of high Cr steel at $600^{\circ}C$, a unified elasto-viscoplastic constitutive model based on the consideration of dislocation density is proposed. A combination of a kinetic equation describing the mechanical response of a material at a given microstructure in terms of dislocation glide and evolution equations for internal variables characterizing the microstructure provides the constitutive equations of the model. Microstructural features of the material such as the grain size and spacing between second phase particles are directly implemented in the constitutive equations. The internal variables are associated with the total dislocation density in a simple model. The model has a modular structure and can be adjusted to describe a creep behavior using the material parameters obtained from uniaxial tensile tests.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.9
• /
• pp.2237-2248
• /
• 1995

In incompressible flow which has multi-length scale, it has a very important effect which dependent variables are used for adaptive grid generation. Among many length scales in incompressible flow, the dependent variables used for the adaptive grid generation should be able to represent the feature of the concerned system. In this paper, by using vorticity and stream function, in addition to velocity components, the smoother and more stable grid generation is possible and these four flow properties represent each scale. The adaptive grid generation for a lid-driven cavity flow with $N_{re}$ =3200 using four flow properties such as velocity components, vorticity, stream function is performed, and the usefulness of using vorticity and stream function as the indicator for adaptive grid generation is shown.

• English Language & Literature Teaching
• /
• v.11 no.2
• /
• pp.1-18
• /
• 2005

This study is a preliminary investigation of the nature of discourse marker acquisition in Korean American speakers of English. Discourse markers are of interest because they are not an aspect of language taught through formal instruction either to native or non-native speakers. Therefore, discourse marker use serves as indirect evidence of face-to-face interaction with native speakers and an indicator of integration. In this light, the present study examines the presence of discourse markers in Korean Americans. The markers chosen for analysis were you know, like, and I mean. The data consist of spontaneous speech elicited from interviews. Sociolinguistic variables such as age, sex, and generation (i.e., $1^{st}$, 1.5, $2^{nd}$) were examined. Results show that there appears to be interaction between the variables and discourse marker use. While all speakers showed variable acquisition of markers, younger, female, and 1.5 generation speakers were found to use discourse markers more than other speakers. Although discourse marker use is optional and thus not a linguistic feature that must be necessarily acquired, it is clear that use is pervasive and acquired differentially by English speakers irrespective of whether they are native or not.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.873-876
• /
• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.

• Structural Engineering and Mechanics
• /
• v.51 no.5
• /
• pp.707-725
• /
• 2014

In order to improve the crane-hook's performance and service life, we formulate a multi-criteria shape design problem considering practical conditions. The structural weight, the displacement at specified points and the induced matrix norm of stiffness matrix are adopted as the evaluation items to be minimized. The heights and widths of cross-section are chosen as the design variables. The design variables are expressed in terms of shape functions based on the Gaussian function. For this multi-objective optimization problem with three items, we utilize a multi-objective evolutionary algorithm, that is, the multi-objective Particle Swarm Optimization (MOPSO). As a common feature of obtained solutions, the side views are tapered shapes similar to those of actual crane-hook designs. The evaluation item values of the obtained designs demonstrate importance of the present optimization as well as the feasibility of the proposed optimal design approach.

• Proceedings of the KIEE Conference
• /
• 2004.11b
• /
• pp.91-93
• /
• 2004

Load forecasting is essential in order to supply electrical energy stably and economically in power systems. ANNs have flexibility to predict a nonlinear feature of load profiles. In this paper, we selected just the necessary input variables used in the paper(2) which is based on the phase-space embedding of a load time-series and reviewing others. So only 5 input variables were selected to forecast for spring, fall and winter season and another input considering temperature sensitivity is added during the summer season. The training cases are also selected from all previous data composed training cases of a 7-day, 14-day and 30-day period. Finally, we selected the training case of a 7-day period because it can be used in STLF without sacrificing the accuracy of the forecast. This allows more compact ANNs, smaller training cases. Consequently, test results show that compact neural networks can be forecasted without sacrificing the accuracy.

• Korean Journal of Remote Sensing
• /
• v.22 no.3
• /
• pp.211-219
• /
• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.