• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.39 no.2
• /
• pp.180-186
• /
• 1994

Lipoxygenase activity of soybean seeds of approximately 507 genotypes as well as its inheritance and selection efficiency in early breeding generation was measured in the Department of Agronomy, Seoul National University to facilitate breeding for low lipoxygenase activity of soybean. Average seed lipoxygenase activity of 507 cultivars and strains was 350 unit (unit: $\Delta$0.01 /min. /mg at 234nm) and ranged from 50 to 670 unit. There was no difference in mean lipoxygenase activity according to apparent seed characters such as seed coat and embryo color. But early mature soybean genotypes had fairly low lipoxygenase activity. Lipoxygenase activity was inherited quantitatively, in which additive effect was greater than dominant one and proportion of gene with positive effects was similar to that with negative ones. Estimated narrow- and broad-sense heritabilities were 0.78 and 0.86 for lipoxygenase activity, respectively. Heritability measured from selection in early breeding lines for high or low lipoxygenase activity was 64~76% or 54~62%, respectively. And selection for high lipoxygenase activity increased by 29.7~44.7%, whereas that for low ones decreased by 21.8~27.3%, respectively, when compared to random population. Clear effect in selecting of lipoxygenase activity was present in early generation.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.2
• /
• pp.171-183
• /
• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Asian-Australasian Journal of Animal Sciences
• /
• v.25 no.9
• /
• pp.1216-1222
• /
• 2012

Somatic cells count (SCC), milk yield (MY) and pedigree information of 2,791 first lactation cows that calved between 1990 and 2010 on 259 Thai farms were used to estimate genetic parameters and trends for SCC and its genetic association with MY. The SCC were log-transformed (lnSCC) to make them normally distributed. An average information-restricted maximum likelihood procedure was used to estimate variance components. A bivariate animal model that considered herd-yr-season, calving age, and regression additive genetic group as fixed effects, and animal and residual as random effects was used for genetic evaluation. Heritability estimates were 0.12 (SE = 0.19) for lnSCC, and 0.31 (SE = 0.06) for MY. The genetic correlation estimate between lnSCC and MY was 0.26 (SE = 0.59). Mean yearly estimated breeding values during the last 20 years increased for SCC (49.02 cells/ml/yr, SE = 26.81 cells/ml/yr; p = 0.08), but not for MY (0.37 kg/yr, SE = 0.87 kg/yr; p = 0.68). Sire average breeding values for SCC and MY were higher than those of cows and dams (p<0.01). Heritability estimates for lnSCC and MY and their low but positive genetic correlation suggested that selection for low SCC may be feasible in this population as it is in other populations of dairy cows. Thus, selection for high MY and low SCC should be encouraged in Thai dairy improvement programs to increase profitability by improving both cow health and milk yield.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.12
• /
• pp.1102-1109
• /
• 2013

Blind interference alignment (BIA) scheme has demonstrated a way of interference alignment (IA) without channel state information at transmitter (CSIT). While it shows superior performance in high signal-to-noise ratio (SNR) regime stemming from the maximal degrees of freedom (DoF) gain, BIA scheme achieves inferior sum-rate performance in low SNR regime. This paper proposes a dual-mode transmission strategy which switches between single user (SU) SISO with receive mode selection and the BIA scheme depending upon the range of SNR. First, we derive a closed-form achievable rate for each transmission-mode. Secondly, we propose a low-complex transmission-mode selection algorithm.

• The KIPS Transactions:PartC
• /
• v.17C no.3
• /
• pp.291-298
• /
• 2010

In wireless sensor networks, energy is the most important consideration because the lifetime of the sensor node is limited by battery. The clustering is the one of methods used to manage network energy consumption efficiently and LEACH(Low-Energy Adaptive Clustering Hierarchy) is one of the most famous clustering algorithms. LEACH utilizes randomized rotation of cluster-head to evenly distribute the energy load among the sensor nodes in the network. The random selection method of cluster-head does not guarantee the number of cluster-heads produced in each round to be equal to expected optimal value. And, the cluster head in a high-density cluster has an overload condition. In this paper, we proposed both a token based cluster-head selection algorithm for guarantee the number of cluster-heads and a cluster selection algorithm for uniform-density cluster. Through simulation, it is shown that the proposed algorithm improve the network lifetime about 9.3% better than LEACH.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.2
• /
• pp.64.2-64.2
• /
• 2016

Optical variability is one way to probe the nature of the central engine of AGN at smaller linear scales and previous studies have shown that optical variability is more prevalent at longer timescales and at shorter wavelengths. Especially, intra-night variability can be explained through the damped random walk model but small samples and inhomogeneous data have made constraining this model hard. To understand the properties and physical mechanism of optical variability, we are performing the KMTNet Active Nuclei Variability Survey (KANVaS). Test data of KMTNet in the COSMOS field was obtained over 2 separate nights during 2015, in B, V, R, and I bands. Each night was composed of 5 and 9 epochs with ~30 min cadence. To find AGN in the COSMOS field, we applied multi-wavelength selection methods. Different selection methods means we are looking different region in unification model of AGN, and 100~120, 400~500, 50~100 number of AGN are detected in X-ray, mid-infrared, and radio selection of AGN, respectively. We performed image convolution to reflect seeing fluctuation, then differential photometry between the selected AGN and nearby stars to achieve photometric uncertainty ~0.01mag. We employed one of the standard time-series analysis tools to identify variable AGN, chi-square test. Preliminarily results indicate that intra-night variability is found for X-ray selected, Type1 AGN are 23.6%, 26.4%, 21.3% and 20.7% in the B, V, R, and I band, respectively. The majority of the identified variable AGN are classified as Type 1 AGN, with only a handful of Type 2 AGN showing evidence for variability. The work done so far confirms that there are type and wavelength dependence of intra-night optical variability of AGN.

• Convergence Security Journal
• /
• v.6 no.3
• /
• pp.117-126
• /
• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• Asian-Australasian Journal of Animal Sciences
• /
• v.28 no.6
• /
• pp.876-887
• /
• 2015

Dicer, an ribonuclease type III type endonuclease, is the key enzyme involved in biogenesis of microRNAs (miRNAs) and small interfering RNAs (siRNAs), and thus plays a critical role in RNA interference through post transcriptional regulation of gene expression. This enzyme has not been well studied in the Indian water buffalo, an important species known for disease resistance and high milk production. In this study, the primary coding sequence (5,778 bp) of bubaline dicer (GenBank: AB969677.1) was determined and the bubaline Dicer1 biocomputationally characterized to determine the phylogenetic signature among higher eukaryotes. The evolutionary tree revealed that all the transcript variants of Dicer1 belonging to a specific species were within the same node and the sequences belonging to primates, rodents and lagomorphs, avians and reptiles formed independent clusters. The bubaline dicer1 is closely related to that of cattle and other ruminants and significantly divergent from dicer of lower species such as tapeworm, sea urchin and fruit fly. Evolutionary divergence analysis conducted using MEGA6 software indicated that dicer has undergone purifying selection over the time. Seventeen divergent sequences, representing each of the families/taxa were selected to study the specific regions of positive vis-$\grave{a}$-vis negative selection using different models like single likelihood ancestor counting, fixed effects likelihood, and random effects likelihood of Datamonkey server. Comparative analysis of the domain structure revealed that Dicer1 is conserved across mammalian species while variation both in terms of length of Dicer enzyme and presence or absence of domain is evident in the lower organisms.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.2
• /
• pp.111-120
• /
• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

• Journal of the Korean Chemical Society
• /
• v.57 no.6
• /
• pp.744-754
• /
• 2013

Quantitative structure-activity relationship (QSAR) analysis for recently synthesized imidazole-(benz)azole and imidazole - piperazine derivatives was studied for their anticancer activities against breast (MCF-7) cell lines. The statistically significant 2D-QSAR models ($r^2=0.8901$; $q^2=0.8130$; F test = 36.4635; $r^2$ se = 0.1696; $q^2$ se = 0.12212; pred_$r^2=0.4229$; pred_$r^2$ se = 0.4606 and $r^2=0.8763$; $q^2=0.7617$; F test = 31.8737; $r^2$ se = 0.1951; $q^2$ se = 0.2708; pred_$r^2=0.4386$; pred_$r^2$ se = 0.3950) were developed using molecular design suite (VLifeMDS 4.2). The study was performed with 18 compounds (data set) using random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression (MLR) methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by kNN-MFA, (k-Nearest Neighbor Molecular Field Analysis) investigating the substitutional requirements for the favorable anticancer activity. The results derived may be useful in further designing novel imidazole-(benz)azole and imidazole-piperazine derivatives against breast (MCF-7) cell lines prior to synthesis.