• Korean Journal of Agricultural Science
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• v.41 no.4
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• pp.481-485
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• 2014

According to statistical data the past ten years, cultivation area and yield of radish are steadily decreasing. This phenomenon cause instability of radish's supply due to meteorological chage, even if radish's yield per unit area is increasing by cultivation technological development. These problems raise radish's price. So, we conducted study on meteorological factors for accuracy improvement of radish yield estimation. Panel analysis was used with two-way effect model considering group effect and time effect. As the result, we show that mixed effects model (fixed effect: group, random effects: time) was statistical significance. According to the model, a rise of one degree in the average air temperature on August will decrease radish's yield per unit area by $428kg{\cdot}10a^{-1}$ and that in the average air temperature on October will increase radish's yield per unit area by $438kg{\cdot}10a^{-1}$. The reason is that radish's growth will be easily influenced by meteorological condition of a high temperature on August and by meteorological condition of a low temperature on Octoboer.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.47-52
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• 2010

The low power of ultra-wideband (UWB) signal makes the acquisition of UWB signal be a more challenging task. In this paper, we propose the method of high order template signal technique that reduces the synchronization time. Experimental results are presented to show the improvements of performance in the mean acquisition time (MAT) and the probability of detection. The performance compared with the serial search, the truly random search and the random permutation search. It is shown that over typical UWB multipath channels, a random permutation search scheme may yield lower MAT than serial search.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.337-337
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• 2006

The synthesis of an octafunctional resorcinarene based initiator for nitroxide mediated polymerization and its ability to yield random star copolymers of styrene and methyl methacrylate is studied. The effect of the initiator conformations towards its activity and the conditions that permit the formation of well-defined star block copolymers is also investigated in detail. The characterization of the initiator and the polymers were carried out by various spectro-analytical techniques. Well-defined random copolymers were obtained with controlled molecular weight and low PDI depending on the monomer feed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.10
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• pp.2491-2497
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• 1993

Random vibration of the coupled identical beams subject to band-limted white noise is studied. The mean-square displacements average dspatially over each beam are derived analytically using two different modal analysis techniques and compared to the results by SEA(Statistical Energy Analysis). It is shown that when frequency is high and a large number of modes are included in the frequency band, the modal analysis methods and the SEA yield the same results provided that the loss factors are very small and the modal separation is much larger than the half-power bandwidth.

• Elastomers and Composites
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• v.56 no.4
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• pp.217-222
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• 2021

In this study, random polypropylene (rPP) was compounded with two of monoglycerides, namely, glyceryl monolaurate (GML) and glyceryl monostearate (GMS), as antifogging agents to improve its antifogging performance. rPP film samples were prepared by a film-casting method using a three-roll casting machine after melt blending through a twin screw extruder. With an increase in the monoglyceride content, the melt flow index for rPP films with GML and GMS increased, and their yield strength decreased. The incorporation of GMS in rPP was proven to be more effective in improving its physical properties than was rPP with GML. When GML and GMS were separately added to the rPP film at contents of more than 1 phr and more than 5 phr, respectively, the film exhibited antifogging performance.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.11
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• pp.1597-1602
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• 2006

The effects of different mineral supplementations on the milk yield of free-ranging Somali camels were investigated in two phases in a semi-arid region of northern Kenya during the dry and wet seasons in 2002 and 2003. In phase 1, twelve (12) lactating camels were selected at random to form four (4) groups each consisting of three camels. The first group served as the control and as a result received no mineral supplementation. In addition to the control diet the other groups received oral doses of minerals as follows over a 60-day period: T1 (P), T2 (High Cu low Co) and T3 (Low Cu high Co). The daily milk yield and blood mineral profiles were measured during the wet and dry seasons. The mean daily milk yield increased from 3.4 L/d to $4.3{\pm}0.3L/d$ and 5.2 L/d in the dry and wet seasons, respectively. Fifteen (15) lactating camels were selected at random to form five groups each consisting of three replicates. The control group did not receive any mineral supplement. The other four groups in addition to the control diet, received the following treatments: T4 (Common Salt), T5 (High Co), T6 (High Co+P) and T7 (Low Co+P). Mineral supplement T6 produced significantly higher milk yield ($5.4{\pm}0.5$ and $6.5{\pm}0.7L/d$) during the dry and wet seasons. Both T6 and T7 had significantly higher milk yield than T4 and T5. During both phases, the blood Ca and P level significantly increased in camels receiving T1, 6 and 7. Animals that received only the trace mineral supplements had lower blood P compared to the ones receiving supplementary P and also the control. Supplementation of lactating camels with Co and P significantly (p<0.05) increased milk yield). Effect of common salt, commonly given by farmers, on milk yield was insignificant. It was concluded that mineral supplementation to lactating camels was beneficial, and that mineral supplements should include P and Co. Further research is required to establish P and Co requirements of lactating camels.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.158-164
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• 2005

Kusukawa and Ohta presented the $CS_{CQ-r}$ chart to monitor the process defect $rate{\lambda}$ in high-yield processes that is derived from the count of defects. The $CS_{CQ-r}$ chart is more sensitive to $monitor{\lambda}$ than the CQ (Cumulative Quantity) chart proposed by Chan et al.. As a more superior chart in high-yield processes, we propose a Synthetic chart that is the integration of the CQ_-r chart and the $CS_{CQ-r}$chart. The quality characteristic of both charts is the number of units y required to observe r $({\geq}2)$ defects. It is assumed that this quantity is an Erlang random variable from the property that the quality characteristic of the CQ chart follows the exponential distribution. In use of the proposed Synthetic chart, the process is initially judged as either in-control or out-of-control by using the $CS_{CQ-r}$chart. If the process was not judged as in-control by the $CS_{CQ-r}$chart, the process is successively judged by using the $CQ_{-r}$chart to confirm the judgment of the $CS_{CQ-r}$chart. Through comparisons of ARL (Average Run Length), the proposed Synthetic chart is more superior to monitor the process defect rate in high-yield processes to the stand-alone $CS_{CQ-r}$ chart.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.151-157
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• 1993

In this paper, we investigate the effects of yield randomness for lot-sizing in a multi-stage production system. The practical importance of incorporating yield randomness into production models has been emphasized by many researchers. Yield randomness, especially in semiconductor manufacturing, poses a mojor challenge for production planning and control. The task becomes even more difficult if the demand for final product is uncertain. An attempt to meet the demand with a higher level of confidence forces one to release more input in the fabrication line. This leads to excessive work-in-process (WIP) inventories which cause jobs to spend unpredictably longer time waiting for the machines. The result is that it is more difficult to meet demand with exceptionally long cycle time and puts further pressure to increase the safety stocks. Due to this spiral effect, it is common to find that the capital tied in inventory is the msot significant factor undermining profitability. We propose a policy to determine the quantity to be processed at each stage of a multi-stage production system in which the yield at each stage may be random and may need rework.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.