• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.05a
• /
• pp.213-216
• /
• 2005

In this paper, we propose the redirect streaming service model to enhance resurgent ability. The system consists of a redirect server, local streaming servers and clients. A redirect server searches the best streaming server. And streaming servers starts a requested service or resumes it when in a trouble. RTSP/RTP is one of the effective solutions to improve QOS in VOD, however a service can be broken off by an overloaded server or network especially in live. We designed and implemented not only a distributed streaming system that solves the broken-off service to enhance a resurgent system, but also DSSP, distributed streaming service protocol, which is adopted to implement this model. Those will improve the performance of streaming service using RTP/RTSP and be contributed to a real time streaming research to solve a service trouble.

• Journal of Korea Multimedia Society
• /
• v.25 no.4
• /
• pp.575-582
• /
• 2022

In the manufacturing environment, research to minimize robot collisions with human beings have been widespread, but in order to interact with robots, it is important to precisely recognize and predict human actions. In this research, after enhancing performance by applying group normalization to the Hourglass model to detect the operator motion, the skeleton was estimated and data were created using this model. And then, three types of operator's movements were recognized using LSTM. As results of the experiment, the accuracy was enhanced by 1% using group normalization, and the recognition accuracy was 99.6%.

• Journal of Korean Society on Water Environment
• /
• v.23 no.6
• /
• pp.927-933
• /
• 2007

Waste activated sludge (WAS) was thermally pretreated to enhance hydrolysis and ultimately methane yield. Batch and semi-continuous anaerobic digestion were conducted to evaluate the performance of methane fermentation of the hydrolyzed sludge and to investigate the kinetics of sludge fermentation. Thermal pretreatment remarkably enhanced digestion performances particularly the methane fermentation with three times more methane production than before the pretreatment. Gas production and kinetic parameters in the semi-continuous anaerobic digestion were estimated using Chen Hashimoto model. The model simulation fitted well the experimental results and the model was shown to be suitable for evaluating the effects of disintegration of WAS in anaerobic digestion. Three parameters ($B_o$, K, and ${\mu}_m$) determined by model simulation were $0.0807L-CH_4/g-VS$, 0.453 and $0.154d^{-1}$ for control sludge, and $0.253L-CH_4/g-VS$, 0.835 and $0.218d^{-1}$ for thermally pretreated sludge, respectively.

• Journal of the Society of Naval Architects of Korea
• /
• v.52 no.1
• /
• pp.1-7
• /
• 2015

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of direct measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model with the genetic programming was suggested for FPSO. The weight estimation model using genetic programming was established by fixing the independent variables based on this data. In addition, the correlation analysis was performed to make up for the weak points of genetic programming; it is apt to induce over-fitting when the number of data is relatively smaller than that of independent variables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, the increasing effect in the number of weight data can be expected. The reliability of the developed weight estimation model was within 2% of error rate.

• Steel and Composite Structures
• /
• v.33 no.4
• /
• pp.583-594
• /
• 2019

In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.

• Smart Structures and Systems
• /
• v.16 no.1
• /
• pp.47-65
• /
• 2015

A novel finite element (FE) model updating method based on multi-resolution analysis (MRA) is proposed. The true stiffness of the FE model is considered as the superposition of two pieces of stiffness information of different resolutions: the pre-defined stiffness information and updating stiffness information. While the resolution of former is solely decided by the meshing density of the FE model, the resolution of latter is decided by the limited information obtained from the experiment. The latter resolution is considerably lower than the former. Second generation wavelet is adopted to describe the updating stiffness information in the framework of MRA. This updating stiffness in MRA is realized at low level of resolution, therefore, needs less number of updating parameters. The efficiency of the optimization process is thus enhanced. The proposed method is suitable for the identification of multiple irregular cracks and performs well in capturing the global features of the structural damage. After the global features are identified, a refinement process proposed in the paper can be carried out to improve the performance of the MRA of the updating information. The effectiveness of the method is verified by numerical simulations of a box girder and the experiment of a three-span continues pre-stressed concrete bridge. It is shown that the proposed method corresponds well to the global features of the structural damage and is stable against the perturbation of modal parameters and small variations of the damage.

• Progress in Superconductivity and Cryogenics
• /
• v.24 no.1
• /
• pp.23-28
• /
• 2022

A sorption cooler, which utilizes helium-4 as a working fluid, was previously developed and tested in KAIST. The cooler consists of a sorption pump and a thermosyphon. The developed sorption cooler aims to pre-cool a certain amount of the magnetic refrigerant of an adiabatic demagnetization refrigerator (ADR) from 4.5 K to 2.5 K. To simulate the high heat capacitance of the magnetic refrigerant, liquid helium was utilized not only as a refrigerant for the sorption cooling but also as a thermal capacitor. The previous experiment, however, showed that the lowest temperature of 2.7 K which was slightly higher than the target temperature (2.5 K) was achieved due to the radiation heat leak. This excessive heat leak would not occur when the sorption cooler is completely integrated with the ADR. Thus, based on the experimentally obtained pumping speed, the prediction model for the sorption cooler is developed in this study. The presented model in this paper assumes the sorption cooler is integrated with the ADR and the heat leak is negligible. The model predicts the amount of the liquid helium and the required time for the sorption cooling process. Furthermore, it is confirmed that the performance of the sorption cooler is enhanced by reducing the volume of the thermosiphon. The detailed results and discussions are summarized.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.4
• /
• pp.305-313
• /
• 2022

The evolution of the GNSS (Global Navigation Satellite System) technology has enhanced positioning performance in terms of positioning accuracy and time efficiency. The technology makes it possible to determine orthometric heights at a few centimeter accuracies by transforming accurate ellipsoid heights if an accurate geoid model has been employed. This study aims to generate a correction surface using GNSS/leveling co-points and a local geoid model, Thailand Geoid Model 2017 (TGM2017), through the Kriging interpolation method in a small local area. Combining the surface and TGM2017 significantly improves height transformation with the 1-cm RMSE (Root Mean Square Error) fit of 10 GNSS/leveling reference points and a mean offset of +0.1 cm. The evaluation of the correction surface at 5 GNSS/leveling checkpoints shows the RMSE of 1.0 cm, which is 82.6 percent of accuracy improvements. The GNSS leveling method can possibly be used to replace a conventional leveling technique at a few centimeter uncertainties in the case of small areas with clear-sky and high satellite visibility environments.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.44-55
• /
• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Tunnel and Underground Space
• /
• v.28 no.3
• /
• pp.277-291
• /
• 2018

LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.