• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.311-328
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• 2017

This study was carried out to investigate the changes in soil microorganisms and soil enzymes by split irrigation and organic matter application under no-tillage green house conditions. Soil bacteria and fungi abundances were higher in soybean cake fertilizer than in the soil without the soybean cake fertilizer under whole quantity irrigation. Bacteria and fungi abundances in soil increased with increasing organic fertilizer application rate. Bacteria and fungi amount in the soil increased at half division irrigation in no-treatment of soybean cake fertilizer compared with whole quantity irrigation. Actinomycete amount in the soil decreased with increasing soybean cake fertilizer with whole quantity irrigation while clearly increased in no-treatment of soybean cake fertilizer. Actinomycete amount in soil clearly increased with increasing organic fertilizer input at half division irrigation. Chitinase activity in the soil decreased in soybean cake fertilizer with increasing organic fertilizer input, while increased in no-treatment of soybean cake fertilizer. Chitinase activity in the soil increased at half division irrigation compared with whole quantity irrigation regardless of soybean cake fertilizer input. ${\beta}$-Glucosidase activity in the soil was higher in soybean cake fertilizer than in no-treatment of soybean cake fertilizer with whole quantity irrigation. ${\beta}$-Glucosidase activity in the soil increased with increasing organic fertilizer input, but decreased in above the standard level 66%. ${\beta}$-Glucosidase activity in the soil clearly increased in no-treatment of soybean cake fertilizer at half division irrigation compared with whole quantity irrigation. N-acetyl-${\beta}$-D-glucosaminidase activity was higher in soybean cake fertilizer than in no-treatment of soybean cake fertilizer with whole quantity irrigation. N-acetyl-${\beta}$-D-glucosaminidase activity in the soil increased with increasing organic fertilizer input, but decreased in above the standard level 66%. N-acetyl-${\beta}$-D-glucosaminidase activity in the soil was not significantly different at half division irrigation and whole quantity irrigation in organic fertilizer input, while increased at half division irrigation in no-treatment of soybean cake fertilizer. Acid phosphatase activity increased at standard level 66% in soybean cake fertilizer, while was not significantly different in no-treatment of soybean cake fertilizer. Spore density of Arbuscular Mycorrhizal Fungi (AMF) in the soil increased with increasing organic fertilizer input at whole quantity irrigation in no-treatment of soybean cake fertilizer, while decreased above the standard level 66% in organic fertilizer input. However, spore density of AMF in the soil was not significantly different in soybean cake fertilizer regardless of input amount of organic fertilizer. Root colonization rate of AMF in red pepper roots was not significant difference at two irrigations regardless of soybean cake input.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1
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• pp.21-29
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• 2006

In this Paper a split bandwidth wideband speech coder and its highband coding method are Proposed. The coder uses a split-band approach. where the wideband input speech signal is split into two equal frequency bands from 0-4kHz and 4-8kHz. The lowband and the highband are coded respectively by the 11.8kb/s G.729 Annex E and the proposed coding method. After the LPC analysis, the highband is divided by two modes according to the properties of signals. In stationary mode. the highband signals are compressed by the mixture excitation model; CELP algorithm and W (Matching Pursuit) algorithm. The others are coded by the only CELP algorithm. We compare the performance of the new wideband speech coder with that of G.722 48kbps SB-ADPCM and G.722.2 12.85kbps in a subjective method. The simulation results show that the Performance of the proposed wideband speech coder has better than that of 48kbps G.722 and no better than that of 12.85kbps G.722.2.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.311-315
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• 2008

The switching fabric used to make high speed switching for packet transfer between input and output interface in recent internet environments. Without making any changes in order to remain ATM switching fabric, the existing structures should split/reassemble a packet to certain size, set aside cross-point buffer and will put loads on the system. In this paper, we proposed a new switch architecture, which has separated data memory plane and switching plane packet data will be stored on the separate memory structure and simultaneously only the part of the memory address pointers can pass the switching fabric. The small mini packets which have address pointer and basic information would be passed through the switching fabric. It is possible to achieve the remarkable switching performance than other switch fabrics with contending variable length packets.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.223-230
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• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.135-140
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• 2013

Noise estimation and detection algorithm to adapt quickly to changing noise environment using the LMS Filter. However, the LMS Filter for noise estimation for a certain period of time and need time to adapt. If the signal changes occur, have the disadvantage of being more adaptive time-consuming. Therefore, noise removal method is proposed to a frame basis AELMS Filter to compensate. In this paper, we split the input signal on a frame basis in noisy environments. Remove the LMS Filter by configuring noise predictions using the mean and variance. Noise, even if the environment changes fast adaptation time to remove the noise. Remove noise and environmental noise and speech input signal is mixed to maintain the unique characteristics of the voice is a way to reduce the damage of voice information. Noise removal method using a frame basis AELMS Filter To evaluate the performance of the noise removal. Experimental results, the attenuation obtained by removing the noise of the changing environment was improved by an average of 6.8dB.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.151-158
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• 2014

In this paper, we introduce an inference system using hard scatter partition method and model the nonlinear process. To do this, we use the hard scatter partition method that partition the input space in the scatter form with the value of the membership degree of 0 or 1. The proposed method is implemented by C-Means clustering algorithm. and is used for the initial center values by means of binary split. by applying the LBG algorithm to compensate for shortcomings in the sensitive initial center value. Hard-scatter-partitioned input space forms the rules in the rule-based system modeling. The premise parameters of the rules are determined by membership matrix by means of C-Means clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. The data widely used in nonlinear process is used to model the nonlinear process and evaluate the characteristics of nonlinear process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.415-423
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• 2019

In this paper, a design method for a high-sensitivity microstrip patch sensor antenna (MPSA) loaded with a bent-slot was studied for the permittivity measurement. The bent-slot similar to a single-ring complementary split ring resonator was added along a radiating edge of the patch in order to enhance the sensitivity to the permittivity. The sensitivity of the proposed MPSA was compared with that of a conventional rectangular MPSA and a thin rectangular-slotted MPSA. Three MPSAs were designed and fabricated on a 0.76-mm-thick RF-35 substrate so that the input reflection coefficient would resonate at 2.5 GHz in the absence of the superstrate under test. When five different Taconic substrates with a relative permittivity ranging from 2.17 to 10.2 were used as the superstrate under test, experiment results show that the sensitivity of the proposed MPSA, which is measured by the shift in the resonant frequency of the input reflection coefficient, is 4.1 to 6.1 times higher than that of the conventional MPSA.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.