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FACTORIZATION OF A HILBERT SPACE ON THE BIDISK
양미혜,홍범일 호남수학회 2009 호남수학학술지 Vol.31 No.4
Let S(z1, z2), S1(z1, z2) and S2(z1, z2) be power series with operator coe±cients such that S(z1, z2) = S1(z1, z2)S2(z1, z2). Assume that the multiplications by S1(z1, z2) and S2(z1, z2) are contractive transformations in H(D2, C). Then the factorizations of spaces D(D, S) and D(D2, S) are well-behaved.
A STUDY OF AVERAGE ERROR BOUND OF TRAPEZOIDAL RULE
양미혜,홍범일 호남수학회 2008 호남수학학술지 Vol.30 No.3
In this paper, to have a better a posteriori error bound of the average case error between the true value of I(f) and the Trapezoidal rule on subintervals using zero mean-Gaussian, we prove that a new average error between the difference of the true value of I(f) from the composite Trapezoidal rule and that of the composite Trapezoidal rule from the simple Trapezoidal rule is bounded by crh2r+3 through direct computation of constants cr for r≤ 2 under the assumption that we have subintervals (for simplicity equal length h) partitioning [0, 1].
AN EXTENDED SPACE DL(S) ASSOCIATED WITH HL(S)
양미혜 대한수학회 2012 대한수학회보 Vol.49 No.3
Let $S$ be a upper triangular operator such that $M_S^L : {\mathcal U}_2 \longrightarrow {\mathcal U}_2 $ defined by $M_S^L (F)=SF$ is a contraction. Then there exists an unitary linear system whose state space is the extension space ${\tilde {\mathcal D}}_L (S)$ associated with ${\mathcal H}_L (S)$.
Machine learning application for predicting the strawberry harvesting time
양미혜,남원호,김태곤,이관호,김영화 충남대학교 농업과학연구소 2019 Korean Journal of Agricultural Science Vol.46 No.2
A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.