• Journal of Animal Environmental Science
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• v.13 no.2
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• pp.139-146
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• 2007

Field experiment was conducted to assess the influence of liquid compost supplemented with chemical fertilizer on the rice growth and yield in 2005. Treatments consisted of liquid compost supplemented with chemical fertilize.(LCSC), liquid manure(LM), chemical fertilizer(CF), LCSC 50% + 50%CF, LCSC 75% + 25%CF. The plant height and tiller's number in plots of 100% basal application plot by LCSC and LM plot were lower than that of chemical fertilizer. But in the plot of basal application by LCSC and top dressing by chemical fertilizer, the height and tilters of rice were significantly difference with LM application plot. And the leaf color the plots by LCSC and chemical fertilizer was darker than that of the LM application plot. In plots by LCSC treatment, number of panicles per hill was higher that of LM plot. As the plot of 100% basal application by LCSC was fertilized, the yield of rice was decreased compared with chemical fertilizer. But the rice yield of basal fertilization by LCSC and top-dressing by chemical fertilizer was 7% increased compared with LM plot. The application plots by LCSC + chemical fertilizer plot were maintained productivity of rice, the rice should be fertilized with the basal application of LCSC and top-dressing of chemical fertilizer. The quality of brown rice was best in LM and LCSC plot, whereas in chemical fertilizer plot, it was the worst by increased of protein content. The results indicates that the application of LCSC + chemical fertilizer improves quality of rice rather quantity.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.315-319
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• 2016

Recurrent neural network based language models (RNN LM) have shown improved results in language model researches. The RNN LMs are limited to post processing sessions, such as the N-best rescoring step of the wFST based speech recognition. However, it has considerable vocabulary problems that require large computing powers for the LM training. In this paper, we try to find the 1st pass N-gram model using word embedding, which is the simplified deep neural network. The class based language model (LM) can be a way to approach to this issue. We have built class based vocabulary through word embedding, by combining the class LM with word N-gram LM to evaluate the performance of LMs. In addition, we propose that part-of-speech (POS) tagging based LM shows an improvement of perplexity in all types of the LM tests.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.165-170
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• 2009

Purpose: Nowadays, NM (in vitro lab) and LM (TLA lab) are overlaped in almost all tests. in this case, how can NM develop continually in a keen competition with LM. we studied to find out current situation and problems after comparing NM with LM. Then, to improve our technic. Methods and meterials: We studied from October 2008 to February 2009 at department of nuclear medicine Seoul National University Bundang Hospital visited 108 patients. We assayed TSH, $FT_4$ by Ria-mat{TSH (n=23), $FT_4$ (n=19)} and AFP, CEA, PSA by manual. {AFP (n=24), CEA (n=31), PSA (n=31)}. On the other hand, LM was measured by TLA system. Results: NM was similar to LM (value of AFP), NMLM (value of PSA,TSH), NM$\leqq$LM (value of $FT_4$ in range 0.01~1.00 ng/mL), NM$\geqq$LM (value of $FT_4$ in range 1.00~6.00 ng/mL) Conclusions: There was no test which result showed big difference remarkably, but several tests have some difference totally or partly. It means that there is possibility to judge wrongly (normal patient->abnormal, abnormal->normal). So, we need to consider always when we report the result.

• MALSORI
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• no.51
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• pp.99-115
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• 2004

In this parer, we propose LM adaptation for broadcast news recognition. We collect information of recent articles from the internet on real time, make a recent small size LM, and then interpolate recent LM with a existing LM composed of existing large broadcast news corpus. We performed interpolation experiments to get the best type of articles from recent corpus because collected recent corpus is composed of articles which are related with test set, and which are unrelated. When we made an adapted LM using recent LM with similar articles to test set through Tf-Idf method and existing LM, we got the best result that ERR of pseudo-morpheme based recognition performance has 17.2 % improvement and the number of OOV has reduction from 70 to 27.

• ETRI Journal
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• v.34 no.5
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• pp.696-705
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• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• Journal of Plant Biotechnology
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• v.42 no.2
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• pp.117-122
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• 2015

Canola is a crop globally used for production of oil and biofuel. Cultivation area and import volume of living modified (LM) canola have been increasing every year. As canola import dependence has reached 100% in Korea, efforts have been made for safety management of LM canola and ecological risk assessment. We developed a set of multiplex PCR method for simultaneous detection of 5 LM canola events (Topas 19/2, Rf3, Ms8, RT73 and T45) approved in Korea. The multiplex PCR assay developed allows amplification of estimated products of 5 LM canolas from event specific primer sets. Primer extension time was skipped for a time-consuming process and two annealing steps (20 cycles at $55^{\circ}C$ and 20 cycles at $60^{\circ}C$) were performed for yielding the best result which was sufficient to distinguish five LM canolas. Our results suggest that multiplex PCR method provides a cost and time-effective approach for LM canola detection.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Journal of The Korean Association For Science Education
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• v.15 no.3
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• pp.263-274
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• 1995

The purpose of this study was to find out effect of problem solving by mental demand of items and chunking level of problem solver on the item. The principal findings of study were as follows ; 1) According to increase of mental demand of items. students' achievement score appeared to decrease and the more mental demand an item needed. the higher or at least the same hierarchical item was. These results showed that mental demand of item was main factor which decided difficulties of problem solving. 2) Though items have the same mental demand. students' achievement score were different between balance beam task and 2nd law task (achievement score of balance beam task < achievement score of 2nd law task). 3) Achievement score of LM group who used chunked knowledge to solve balance beam task were higher than non LM group who used non chunked knowledge. 4) The level of chunked knowledge was different between two tasks when non LM group solved items of two tasks. On the other hand, LM group used the same level of chunked knowledge to solve items of two tasks. 5) Achievement score of non LM group was the same between items of two tasks after treatment due to chunking effect by treatment. But achievement score of LM group didn't change before and after treatment. The chunking effect by treatment had an effect on non LM group, but it was not on LM group.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.327-340
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• 2008

This paper derives joint and conditional Lagrange multiplier tests based on information matrix for testing functional form and/or the presence of autocorrelation in a regression model. Small sample properties of these tests are assessed by Monte Carlo study and comparisons are made with LM tests based on Hessian matrix. The results show that the proposed $LM_E$ tests have the most appropriate finite sample performance.

• Journal of Microbiology and Biotechnology
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• v.27 no.3
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• pp.598-609
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• 2017

Pediococcus pentosaceus ID-7 was isolated from kimchi, a Korean fermented food, and it showed high activity for lactose hydrolysis. The ${\beta}$-galactosidase of P. pentosaceus ID-7 belongs to the GH2 group, which is composed of two distinct proteins. The heterodimeric LacLM type of ${\beta}$-galactosidase found in P. pentosaceus ID-7 consists of two genes partially overlapped, lacL and lacM encoding LacL (72.2 kDa) and LacM (35.4 kDa). In this study, Escherichia coli MM294 was used for the production of LacL, LacM, and LacLM. These three types of recombinant proteins were expressed, purified, and characterized. The specific activities of LacLM and LacL were 339 and 31 U/mg, respectively. However, activity was not detected with LacM alone. The optimal pH of LacLM and LacL was pH 7.5 and pH 7.0, and the optimal temperature of LacLM and LacL was $40^{\circ}C$ and $50^{\circ}C$, respectively. The optimal temperature changes indicate that LacLM is able to achieve higher activity at a relatively lower temperature. LacLM was strongly activated by $Mg^{2+}$, $Mn^{2+}$, and $Zn^{2+}$, which was not true for LacL. Consistent with this, EDTA strongly inactivated LacLM and LacL, but the presence of reducing agents did not dramatically alter the activity. Taken together, multiple alignment of amino acid sequences and phylogenetic analysis results of LacL and LacM of P. pentosaceus ID-7 suggest the evolution of LacL into LacLM and that the use of divalent metal ions results in higher activity.