• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.59-59
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• 2017

Rice (Oryza sativa L.) is the most important crop and its yield must be improved to feed the increasing global population. Recently developed high-yielding varieties with extra-large sink capacity often have a problem in unstable grain-filling. Therefore, understanding limiting factors for improving grain-filling and controlling them are essential for further improvement of rice grain yield. However, since grain-filling rate was determined by complex sink-source balance, the ability of grain-filling was very difficult to evaluate. Source ability for 'grain' was not only determined by the ability of carbon assimilation in leaves, but also that of carbon translocation from leaves to panicles. Sink strength was determined by the complex carbon metabolism from sucrose degradation to starch synthesis. Hence, to evaluate the grain-filling ability and determine its regulatory steps, the whole picture of carbon flow from photosynthesis at leaves to starch synthesis at grains must be revealed in a metabolite level. In this study, the yield and grain growth rate of three high-yielding varieties, which show high sink capacity commonly, were compared. Momiroman showed lower grain filling rate and slower grain growth rate than the other varieties, Hokuriku 193 and Tequing. To clarify the limiting point in the carbon flow of Momiroman, $CO_2$ labeled by stable isotope ($^{13}C$) was fed to three varieties during ripening period. The ratio of $^{13}C$ left in the stem was higher in Momiroman 24 hours after feeding, suggesting inefficient carbon translocation of Momiroman. More interestingly, $^{13}C$ translocation from soluble fraction to insoluble one in the grain seemed to be slower in Momiroman. To get the further insight in a metabolite level, we are now trying the $^{13}C$ labeling metabolome analysis in the developing grains.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.65-78
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• 2010

In Video Software Dealers Association v. Arnold Schwarzenegger, the federal 9th Circuit Court decided that a California law imposing restrictions and a labeling requirement on the sale or rental of violent video games to minors (the "Act") violated rights guaranteed by the First and Fourteenth Amendments to the United States Constitution because: (1) the state introduced insufficient evidence to support a compelling interest that video games created psychological or neurological harm, (2) the Act was not the least-restrictive alternative to negate the harm, and (3) the lower, rational basis standard applicable to commercial speech did not apply to the Act's labeling requirements because the required label did not convey factual information. On the contrary, Korean Constitutional Court decided that "Harmful Medium to Youth" and "Preliminary Rate Classification" would be constitutional. However, under the least-restrictive method rule of the U. S. Court and Korean Court, overlap application of "Harmful Medium to Youth" and "Preliminary Rate Classification" could be a problem and it would be possible that stronger regulation among these would be found as unconstitutional.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.661-690
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• 2022

Adolescent delinquency is a substantial social problem that occurs in both offline and online domains. The current study utilized random forest algorithms to identify predictors of adolescents' online and offline delinquency. Further, we explored the applicability of classic delinquency theories (social learning, strain, social control, routine activities, and labeling theory). We used the first-grade and fourth-grade elementary school panels as well as the first-grade middle school panel (N=4,137) among the sixth wave of the nationally-representative Korean Children and Youth Panel Survey 2010 for analysis. Random forest algorithms were used instead of the conventional regression analysis to improve the predictive performance of the model and possibly consider many predictors in the model. Random forest algorithm results showed that classic delinquency theories designed to explain offline delinquency were also applicable to online delinquency. Specifically, salient predictors of online delinquency were closely related to individual factors(routine activities and labeling theory). Social factors(social control and social learning theory) were particularly important for understanding offline delinquency. General strain theory was the commonly important theoretical framework that predicted both offline and online delinquency. Findings may provide evidence for more tailored prevention and intervention strategies against offline and online adolescent delinquency.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.536-543
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• 2023

Speaker diarization, which labels for "who spoken when?" in speech with multiple speakers, has been studied on a deep neural network-based end-to-end method for labeling on speech overlap and optimization of speaker diarization models. Most deep neural network-based end-to-end speaker diarization systems perform multi-label classification problem that predicts the labels of all speakers spoken in each frame of speech. However, the performance of the multi-label-based model varies greatly depending on what the threshold is set to. In this paper, it is studied a speaker diarization system using single-label classification so that speaker diarization can be performed without thresholds. The proposed model estimate labels from the output of the model by converting speaker labels into a single label. To consider speaker label permutations in the training, the proposed model is used a combination of Permutation Invariant Training (PIT) loss and cross-entropy loss. In addition, how to add the residual connection structures to model is studied for effective learning of speaker diarization models with deep structures. The experiment used the Librispech database to generate and use simulated noise data for two speakers. When compared with the proposed method and baseline model using the Diarization Error Rate (DER) performance the proposed method can be labeling without threshold, and it has improved performance by about 20.7 %.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.132-133
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• 2003

The marine dinoflagellate genus Alexandrium comprise PSP producing A. acatenella, A. angustitabuzatum, A. catenella, A. fundyense, A. minutum, A. ostenfezdii, A. tamiyavanichii and A. tamarense. In monitoring toxic Alexandrium, rapid and exact species identification is one of the significant prerequisite work, however we have suffered confusion of species definition in Alexandrium. To surmount this problem, we chose DNA probing, which has long been used as an alternative for conventional identification methods, primarily relying on morphological approaches using microscope in microbial field. Oligonucleotide DNA probes targeting rRNA or rDNA have been commonly used in diverse studies to detect and enumerate cells concerned as a culture-indetendent powerful tool. Despite of the massive literature on the HAB species containing Alexandrium, application of DNA probing for species identification and detection has been limited to a few documents. DNA probes of toxic A. tamarense, A. catenella and A. tamiyavanichii, and non-toxic A. affine, A. fraterculus, A. insuetum and A. pseudogonyaulax were designed from LSU rDNA D1-D2, and applied to whole cell-FISH. Each DNA probes reacted only the targeted Alexandrium cells with very high species-specificity within Alexandrium. The probes could detect each targeted cells obtained from the natural sea water samples without cross-reactivity. Labeling intensity varied in the growth stage, this showed that the contents of probe-targeted cellular rRNA decreased with reduced growth rate. Double probe TAMID2S1 achieved approximately two times higher fluorescent intensity than that with single probe TAMID2. This double probe did not cross-react with any kinds of microorganisms in the natural sea waters. Therefore we can say that in whole-cell FISH procedure this double DNA probe successfully labeled targeted A. tamiyavanichii without cross-reaction with congeners and diverse natural bio-communities.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.451-456
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• 2017

Semantic segmentation, which is the most basic and complicated problem in computer vision, classifies each pixel of an image into a specific object and performs a task of specifying a label. MRF and CRF, which have been studied in the past, have been studied as effective methods for improving the accuracy of pixel level labeling. In this paper, we propose a semantic partitioning method that combines CNN, a kind of deep running, which is in the spotlight recently, and CRF, a probabilistic model. For learning and performance verification, Pascal VOC 2012 image database was used and the test was performed using arbitrary images not used for learning. As a result of the study, we showed better partitioning performance than existing semantic partitioning algorithm.

• Journal of Korea Port Economic Association
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• v.31 no.4
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• pp.151-168
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• 2015

Shippers face uncertainty and risks until the delivery of goods to the buyer (importer). To avoid these uncertainty and risks, shippers use the abroad logistics center and then try to construct continuous contract relations. From this viewpoint, this study examines the motivations of shippers to take advantage of the logistics center and perception difference for service quality according to the characteristics of shippers. For this purpose, T-test and ANOVA analyses are conducted using SPSS 21.0. The results suggest the following implications. First, there are no differences in perception regarding the motivation to take advantage of the logistics center for the size and characteristics of products. Second, the main motivating factors are maneuver to competitors and meeting buyers' demands by using the abroad logistics center. Furthermore, there is the level of perception for service quality regarding packaging and labeling in the logistics center. In contrast, the problem process and the quality of the order progress information is higher. Finally, specific logistics services are required depending on individual products because each product's characteristics are different.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.231-241
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• 2006

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method for training avatar behaviors from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on machine learning technique, called Q-teaming, our training method allows the avatar to learn how to act in any given situation through trial-and-error interactions with a dynamic environment. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.1-13
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• 2008

In this paper, we propose a method to solve a problem in binarizing the rear number plate image captured by a camera on a moving vehicle. An image may be shadowed by the cavernous structure of the rear side of a moving vehicle and it makes us hard to get a high quality of binary image. Therefore, we first detect a shadow edge and then divide an image into the shadow part and non-shadow part by the edge. Finally, the binary image is obtained by binarizing each part and merging them In this paper, we do comparative work on a group of binarization methods including our method, the method suggested by Zheng, the method using block binarization, and the method using labeling. The result shows that our method achieves better performance than others in most cases.