• Journal of the Korean Magnetic Resonance Society
• /
• v.13 no.1
• /
• pp.7-14
• /
• 2009

The spectral data were collected from the two 600 MHz spectrometers from the two major manufacturers, Broker and Varian. The samples were prepared to create standard curves for quantitative measurements of metabolite concentrations. Instead of employing one-dimensional $^1H$ experiments, the two-dimensional $^1H-^{13}C$ HSQC experiments were performed for better separation of resonances. For some resonances, the high salt condition hindered the linear correlation between the intensity and actual metabolite concentration. Excluding overlapped ones, most resonances showed good linearity. Although the Varian spectrometer showed better linearity, both spectrometers were able to generate acceptable standard curves. From this data, we could identify resonances that could be used to better quantify the concentrations of the particular metabolites. With these standard curves, the quantitative measurements of the metabolites from the real samples will be facilitated.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.12B
• /
• pp.767-778
• /
• 2007

In this paper, we propose a Intelligent Service Reasoning (ISR) model using data mining in smart home environments. Our model creates a service tree used for service reasoning on the basis of C4.5 algorithm, one of decision tree algorithms, and reasons service that will be offered to users through quantitative weight estimation algorithm that uses quantitative characteristic rule and quantitative discriminant rule. The effectiveness in the performance of the developed model is validated through a smart home-network simulation.

• Journal of the Korean Society of Safety
• /
• v.33 no.5
• /
• pp.141-149
• /
• 2018

In a PCB factory, there is a corrosive chemical substance supply system that can causes major leakage accidents. These accidents can give rise to shut down the factory and do residents damage that cause enormous loss of properties. To mitigate these risks, it is necessary to provide a chemical disaster prevention system. Moreover, after considering the situation and environment of the production site, it is of great importance to build an optimal chemical accident prevention system by reflecting risk reduction measures from the point of process design and by assessing quantitative risk based on reliability data. However, because there was no established database of the reliability about facilities and equipment that can be used in the domestic, the business site and consulting organization had being used the reliability data such as USA CCPS(Center for Chemical Process Safety). In these days, Korean institutes are studying on reliability data utilization method of quantitative risk assessment for preventing chemical accidents and domestic utilization algorithms and storage bed of reliability data. This study presents samples of reliability database about the chemical substance supply system that constructed from the history data such as failure, maintenance for 10 years at a PCB factory. Also, this work proposes the safety design criteria for supply facilities of corrosive chemical substance by assessing quantitative risk on the basis of the reliability data.

• Journal of Korean Society for Quality Management
• /
• v.46 no.3
• /
• pp.441-452
• /
• 2018

Purpose: The purpose of this paper is to study quantitative risk grade assessment for objective government quality assurance activities based on risk management in initial mass production for weapon systems. Methods: The Defense quality management regulations and foreign risk assessment documents are referred to analyze problems performing quality assurance actives. The failure rate data, maintainability and cost of products have been studied to quantify the risk Likelihood and impact. The analyzed data were classified as risk grade assessment through K-means Cluster Analysis method. Results: Results show that a proposed method can objectively evaluate risk grade. The analyzed results are clustered into three levels such as high, middle and low. Two products are allocated high, eleven low and seven middle. Conclusion: In this paper, quantitative risk grade assessment methods were presented by analyzing risk ratings based on objective data. The findings showed that the methods would be effective for initial mass production for weapon systems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.3
• /
• pp.316-321
• /
• 2007

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.

• Communications for Statistical Applications and Methods
• /
• v.27 no.6
• /
• pp.701-714
• /
• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Journal of Korea Technology Innovation Society
• /
• v.20 no.2
• /
• pp.271-291
• /
• 2017

This study measures and compares the performance of research and development (R&D) programs in government-funded research institutes (GRIs) in terms of qualitative and quantitative approaches to find out strategic insights for improving performance evaluation policy. In particular, we adopt the evaluation results from the real data of K institute in 2015 for a qualitative evaluation and the results of data envelopment analysis (DEA) for a quantitative evaluation. Comparative analysis of the R&D performance of 14 programs finds that the difference between the evaluation results of qualitative and quantitative approaches is significant. From this finding, we suggest several strategic directions to complement two approaches each other.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.909-914
• /
• 2024

This study suggests a quantitative evaluation of transfer learning, which is widely used in various AI fields, including image recognition for robot vision. Quantitative and qualitative analyses of results applying transfer learning are presented, but transfer learning itself is not discussed. Therefore, this study proposes a quantitative evaluation of transfer learning itself based on MNIST, a handwritten digit database. For the reference network, the change in recognition accuracy according to the depth of the transfer learning frozen layer and the ratio of transfer learning data and pre-training data is tracked. It is observed that when freezing up to the first layer and the ratio of transfer learning data is more than 3%, the recognition accuracy of more than 90% can be stably maintained. The transfer learning quantitative evaluation method of this study can be used to implement transfer learning optimized according to the network structure and type of data in the future, and will expand the scope of the use of robot vision and image analysis AI in various environments.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2016.09a
• /
• pp.25-28
• /
• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.183-183
• /
• 2020

Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.