• KOREAN JOURNAL OF CROP SCIENCE
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• v.23 no.2
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• pp.34-39
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• 1978

Partial dominance was exhibited by flowering(heading), panicle length, panicle numbers, $F_1$ag leaf length, 1000 kernel weight in $F_1$ and $F_2$ hybrids by 7 \times 7 and 5 \times 5 diallel crosses. Over dominance was exhibited by culm length, flag leaf width, appearance degree of panicle in $F_1$ generation of the crosses, and also over dominance was exhibited by kernel weight in $F_2$ generation of the crosses.

• Journal of KIISE
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• v.42 no.2
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• pp.190-202
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• 2015

Much research has been conducted on location-based intelligent personal assistants that can understand a user's intention by learning the user's route model and then inferring the user's destinations and routes using data of GPS and other sensors in a smartphone. The intelligence of the location-based personal assistant is contingent on the accuracy and efficiency of the real-time predictions of the user's intended destinations and routes by processing movement information based on uncertain sensor data. We propose a robust particle filter based on Dynamic Bayesian Network model to infer the user's routes. The proposed robust particle filter includes a particle generator to supplement the incorrect and incomplete sensor information, an efficient switching function and an weight function to reduce the computation complexity as well as a resampler to enhance the accuracy of the particles. The proposed method improves the accuracy and efficiency of determining a user's routes and destinations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2A
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• pp.99-108
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• 2002

In this paper, we discuss the effect of imperfect knowledge of the transmission channel on the M-QAM SER performance of space-time block codes. Because the channel knowledge is used for decoding of space-time block codes, the imperfect channel knowledge can degrade the performance of space-time block codes. In this paper, the channel mismatch error is modeled as errors in the estimation of the channel due to noise and errors due to the variation of the channel. We derive the analytic expression for the symbol error rate (SER) as a function of the average signal to interference ratio (SIR) per channel including the terms of channel mismatch errors. Simulation results show that the acceptable levels of channel estimation error is 10$\^$-3/ and that of channel variation is f$\_$d/T$\_$B/=0.001 at SNR=20dB in space-time block codes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3B
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• pp.183-198
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• 2006

We define the incomplete medium sharing system as a multi-channel shared medium communication system where constraints are imposed to the set of channels that may be allocated to some transmitter-receiver node pairs. To derive a centralized MAC scheme of a incomplete medium sharing system, we address the problem of optimal channel allocation The optimal channel allocation problem is then translated into a max-flow problem in a multi-commodity flow graph, and it is shown that the optimal solution can then be obtained by solving a linear programming problem. In addition, two suboptimal channel allocation schemes are proposed to bring down the computational complexity to a practical/feasible level; (1) one is a modified iSLIP channel allocation scheme, (2) the other is sequential channel allocation scheme. From the results of a extensive set of numerical experiments, it is found that the suboptimal schemes evaluate channel utilization close to that of the optimal schemes while requiring much less amount of computation than the optimal scheme. In particular, the sequential channel allocation scheme is shown to achieve higher channel utilization with less computational complexity than . the modified iSLIP channel allocation scheme.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.133-138
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• 2006

Learning with Attribute Value Taxonomies (AVT) has shown that it is possible to construct accurate, compact and robust classifiers from a partially missing dataset (dataset that contains attribute values specified with different level of precision). Yet, in many cases AVTs are generated from experts or people with specialized knowledge in their domain. Unfortunately these user-provided AVTs can be time-consuming to construct and misguided during the AVT building process. Moreover experts are occasionally unavailable to provide an AVT for a particular domain. Against these backgrounds, this paper introduces an AVT generating method called GA-AVT-Learner, which finds a near optimal AVT with a given training dataset using a genetic algorithm. This paper conducted experiments generating AVTs through GA-AVT-Learner with a variety of real world datasets. We compared these AVTs with other types of AVTs such as HAC-AVTs and user-provided AVTs. Through the experiments we have proved that GA-AVT-Learner provides AVTs that yield more accurate and compact classifiers and improve performance in learning missing data.

• Korean Journal of Logic
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• v.14 no.3
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• pp.101-137
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• 2011

Based on his analysis of judgement forms in intuitionistic type theory, Martin-L$\ddot{o}$f claims that the usual logical laws and interesting mathematical judgements are synthetic, not analytic. He further claims that the logic of analytic judgements is decidable and complete, while the logic of synthetic judgements is undecidable and incomplete. The aim of this article is to clarify and examine his claims. In section 1, I explain and give some comments on the monomorphic version of intuitionistic type theory. In section 2, after clarifying Martin-L$\ddot{o}$f's distinction between analytic and synthetic judgements, I examine some possible objections to it and evaluate the thesis that the usual logical laws and interesting mathematical judgements are synthetic. In section 3, I clarify and examine the thesis that the logic of analytic judgements is decidable and complete, while the logic of synthetic judgements is undecidable and incomplete.

• Journal of Veterinary Clinics
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• v.28 no.5
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• pp.526-529
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• 2011

The brachiocephalic trunk (Bct) branches from the aortic arch (Aa) and consists, in ruminants, of the common trunk of the left subclavian artery (LSb), the bicarotid artery (Bc) or left and right common carotid artery (LCc and RCc), and the right subclavian artery (RSb). This pattern differs from the primitive mammalian Aa pattern due to the fact that the analogs of the LCc and LSb migrate cranially and merge with the common trunk of the RCc and RSb in the embryonic stage. A Bct having a septal remnant that consisted of the tunica media was observed in a female Korean water deer (Hydropotes inermis argyropus), which was deemed to have resulted from an incomplete merging of the vessel walls between a carnivoran-type Bct and an incomplete LSb. This is the first report of an abnormal Bct in a Korean water deer.

• 사회경제평론
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• v.31 no.1
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• pp.63-105
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• 2018

Keynes proposed revolutionary claims in his General Theory that the capitalist economy have the following characteristics: unemployment is general, unemployment could not be automatically restored, business cycle and crisis are inherent in the capitalist economy. Hicks refuted Keynes that unemployment is a special case of depression, and Modigliani argued that it is only valid under a particular assumption of wage rigidity. Also, Pigou and Patinkin contended that unemployment can be automatically recovered in the flexible wages and prices system. These refutations have made the revolutionary reforms appeared in Keynes's General Theory to decline. However, their claims did not correctly interpret Keynes's theories, nor effectively refute them. They interpreted Keynes narrowly within the framework of the classical tradition and refuted Keynes using the claims of the classics. The revolutionary nature of Keynes's General Theory could be not undermined by their refutation, but rather may be more useful in analyzing today's economic reality.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.149-159
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• 2019

Six sigma is an innovative management movement which provides improved business process by adapting the paradigm and the trend of market and customers. Suitable selection of six sigma project could highly reduce the costs, improve the quality, and enhance the customer satisfaction. There are existing studies on the selection of Six Sigma projects, but few studies have been conducted to select the correct project under an incomplete information environment. The purpose of this study is to propose the application of integrated MCDM techniques for correct project selection under incomplete information. The project selection process of six sigma involves four steps as follows: 1) determination of project selection criteria 2) calculation of relative importance of team member's competencies 3) assessment with project preference scale 4) finalization of ranking the projects. This study proposes the combination methods by applying group fuzzy Analytical Hierarchy Process (AHP), an easy defuzzified number of Trapezoidal Fuzzy Number (TrFN) and Grey Relational Analysis (GRA). Both of the weight of project selection criteria and the relative importance of team member's competencies can be evaluated by group fuzzy AHP. Project preferences are assessed by easy defuzzified scale of TrFN in case of incomplete information.)

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.1-6
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• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.