• Korean Journal of Materials Research
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• v.34 no.2
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• pp.85-94
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• 2024

The optimization of deacetylation process parameters for producing chitosan from isolated chitin shrimp shell waste was investigated using response surface methodology with central composite design (RSM-CCD). Three independent variables viz, NaOH concentration (X₁), radiation power (X₂), and reaction time (X₃) were examined to determine their respective effects on the degree of deacetylation (DD). The DD of chitosan was also calculated using the baseline approach of the Fourier Transform Infrared (FTIR) spectra of the yields. RSM-CCD analysis showed that the optimal chitosan DD value of 96.45 % was obtained at an optimized condition of 63.41 % (w/v) NaOH concentration, 227.28 W radiation power, and 3.34 min deacetylation reaction. The DD was strongly controlled by NaOH concentration, irradiation power, and reaction duration. The coefficients of correlation were 0.257, 0.680, and 0.390, respectively. Because the procedure used microwave radiation absorption, radiation power had a substantial correlation of 0.600~0.800 compared to the two low variables, which were 0.200~0.400. This independently predicted robust quadratic model interaction has been validated for predicting the DD of chitin.

• Clinical and Experimental Vaccine Research
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• v.12 no.4
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• pp.337-345
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• 2023

Purpose: The global fight against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has led to widespread vaccination efforts, yet the optimal dosing schedule for SARS-CoV-2 vaccines remains a subject of ongoing research. This study aims to investigate the effectiveness of administering two booster doses as the third and fourth doses at different intervals to enhance vaccine protection. Materials and Methods: This study was conducted at a military regional hospital operated by the Ministry of National Defense in Taiwan. A cohort of vaccinated individuals was selected, and their vaccine potency was assessed at various time intervals following their initial vaccine administration. The study participants received booster doses as the third and fourth doses, with differing time intervals between them. The study monitored neutralizing antibody titers and other relevant parameters to assess vaccine efficacy. Results: Our findings revealed that the potency of the SARS-CoV-2 vaccine exhibited a significant decline 80 days after the initial vaccine administration. However, a longer interval of 175 days between booster injections resulted in significantly higher neutralizing antibody titers. The individuals who received the extended interval boosters exhibited a more robust immune response, suggesting that a vaccine schedule with a 175-day interval between injections may provide superior protection against SARS-CoV-2. Conclusion: This study underscores the importance of optimizing vaccine booster dosing schedules to maximize protection against SARS-CoV-2. The results indicate that a longer interval of 175 days between the third and fourth doses of the vaccine can significantly enhance the neutralizing antibody response, potentially offering improved protection against the virus. These findings have important implications for vaccine distribution and administration strategies in the ongoing battle against the SARS-CoV-2 pandemic. Further research and largescale trials are needed to confirm and extend these findings for broader public health implications.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.83-93
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• 2002

To improve the performance of estimation, the research that uses additional information addition to traffic count and target OD with additional survey cost have been studied. The purpose of this paper is to improve the performance of OD estimation by reducing the feasible solutions with cost-efficiently additional information addition to traffic counts and target OD. For this purpose, we Propose the OD estimation method with sample link use proportion as additional information. That is, we obtain the relationship between OD trip and link flow from sample link use proportion that is high reliable information with roadside survey, not from the traffic assignment of target OD. Therefore, this paper proposes OD estimation algorithm in which the conservation of link flow rule under the path-based non-equilibrium traffic assignment concept. Numerical result with test network shows that it is possible to improve the performance of OD estimation where the precision of additional data is low, since sample link use Proportion represented the information showing the relationship between OD trip and link flow. And this method shows the robust performance of estimation where traffic count or OD trip be changed, since this method did not largely affected by the error of target OD and the one of traffic count. In addition to, we also propose that we must set the level of data precision by considering the level of other information precision, because "precision problem between information" is generated when we use additional information like sample link use proportion etc. And we Propose that the method using traffic count as basic information must obtain the link flow to certain level in order to high the applicability of additional information. Finally, we propose that additional information on link have a optimal counting location problem. Expecially by Precision of information side it is possible that optimal survey location problem of sample link use proportion have a much impact on the performance of OD estimation rather than optimal counting location problem of link flow.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.895-904
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• 2007

This paper proposes a robust sub-optimal smart antenna in Code Division Multiple Access (CDMA) basestation. It makes use of the property of the Least Mean Square (LMS) algorithm and the Conjugate Gradient Method (CGM) algorithm for beamforming processes. The weight update takes place at symbol level which follows the PN correlators of receiver module under the assumption that the post correlation desired signal power is far larger than the power of each of the interfering signals. The proposed algorithm is simple and has as low computational load as five times of the number of antenna elements(O(5N)) as a whole per each snapshot. The output Signal to Interference plus Noise Ratio (SINR) of the proposed smart antenna system when the weight vector reaches the steady state has been examined. It has been observed in computer simulations that proposed beamforming algorithm improves the SINR significantly compared to the single antenna case. The convergence property of the weight vector has also been investigated to show that the proposed hybrid algorithm performs better than CGM and LMS during the initial stage of the weight update iteration. The Bit Error Rate (BER) characteristics of the proposed array has also been shown as the processor input Signal to Noise Ratio (SNR) varies.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.504-513
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• 2017

This study investigated one of the controversial issues on debate or even controversial between policy makers at the government and corporate levels: To examine any financial determinants on the cash holdings of the firms in the advanced and emerging capital markets. Futhermore, it focused on the large representative firms headquartered in the U.S. and the Republic of Korea, taking into account scarcity of the previous literature concentrated on the comparative studies on this particular subject. Several legitimate, but robust econometric estimations such as static and dynamic panel data models and Tobit regression, were applied to investigate possible financial factors ono the cash liquidity. Given the continued debates or arguments on the excess cash reserves between interest partied at the government and corporate levels in the advanced and/or emerging capital markets, and more accelerated capital transfers among associated nations by engaging in the arrangements of the FTAs, the results of the study may provide a vision to search for the optimal level of corporate cash holdings for firms in the two nations.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.41-47
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• 2009

Routing through a backbone, which is responsible for performing and managing multipoint communication, reduces the communication overhead and overall energy consumption in wireless sensor networks. However, the backbone nodes will need extra functionality and therefore consume more energy compared to the other nodes. The power consumption imbalance among sensor nodes may cause a network partition and failures where the transmission from some sensors to the sink node could be blocked. Hence optimal construction of the backbone is one of the pivotal problems in sensor network applications and can drastically affect the network's communication energy dissipation. In this paper a distributed algorithm is proposed to generate backbone trees through robust multi-hop clusters in wireless sensor networks. The main objective is to form a properly designed backbone through multi-hop clusters by considering energy level and degree of each node. Our improved cluster head selection method ensures that energy is consumed evenly among the nodes in the network, thereby increasing the network lifetime. Comprehensive computer simulations have indicated that the newly proposed scheme gives approximately 10.36% and 24.05% improvements in the performances related to the residual energy level and the degree of the cluster heads respectively and also prolongs the network lifetime.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.255-263
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• 2016

This study conducted empirical tests on contemporary finance theories for corporate cash holdings, such as trade-off, pecking order, and agency theory. There is ongoing debate on the possibility of excess cash savings by domestic firms, including chaebols in the Korean capital markets. Thus, it may be worthy to identify any financial characteristics based on each aforementioned theory as an extension of previous studies on similar subjects. Two primary hypotheses were postulated and tested, and the following empirical results were obtained. First, principal component analysis (PCA) provides evidence that nine out of the twenty explanatory variables showed a significant influence on the level of corporate cash holdings, such as cash conversion cycle in trade-off theory and leverage in pecking order theory. Second, the chaebol firms that decreased cash holdings after global financial turmoil may be affected by financial factors that include investment opportunities and foreign ownership according to the PCA. The results may reinforce the outcomes derived from previous research on corporate cash holdings. Based on the robust results, large firms in advanced or emerging capital markets could approach the optimal level of the cash reserves.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.11-22
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• 2007

This paper is to discuss analytical techniques to estimate demand sizes and volumes that determine optimal locations for multiple facilities for a given services. While demand size estimation is a core part of location modeling to enhance solution quality and practical applicability, the estimation method has been used in limited and restrict parts such as a single population centroid in a given larger census boundary area or small theoretical application experiments(e.s. census track and enumeration district). Therefore, this paper strives to develop an analytical estimation method of demand size that converts area based demand data to point based population weighted centroids. This method is free to spatial boundary units and more robust to estimate accurate demand volumes regardless of geographic boundaries. To improve the estimation accuracy, this paper uses house weighted value to the population centroid calculation process. Then the population weighted centroids are converted to individual demand points on a grid formated surface area. In turn, the population weighted centroids, demand points and network distance measures are operated into location-allocation models to examine their roles to enhance solution quality and applicability of GIS location models. Finally, this paper demonstrates the robustness of the weighted estimation method with the application of location-allocation models.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.