• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.68-70
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• 2002

Prediction of 3-D permeability tensor for multi-axial preform is critical to model and design the manufacturing process of composites by considering resin flow through the multi-axial fiber structure. In this study, the in-plane and transverse permeabilities for braided preform are predicted numerically. The flow analyses are calculated by using 3-D CVFEM(control volume finite element method) for macro-unit cells. To avoid checker-board pressure field and improve the efficiency of numerical computation, a new interpolation function for velocity is proposed on the basis of analytic solutions. Permeability of a braided preform is measured through unidirectional flow experiment and compared with the permeability calculated numerically. Unlike other studies, the current study is based on more realistic unit cell and prediction of permeability is improved.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.235-244
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• 2022

Many new techniques have been adopted in HEVC (High efficiency video coding) standard, such as quadtree-structured coding unit (CU), prediction unit (PU) partition, 35 intra-mode, and so on. To reduce computational complexity, the paper proposes two optimization algorithms which include fast CU depth range decision and fast PU partition mode decision. Firstly, depth range of CU is predicted according to spatial-temporal correlation. Secondly, we utilize the depth difference between the current CU and CU corresponding to the same position of adjacent frame for PU mode range selection. The number of traversal candidate modes is reduced. The experiment result shows the proposed algorithm obtains a lot of time reducing, and the loss of coding efficiency is inappreciable.

• 연구논문집
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• s.23
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• pp.45-56
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• 1993

In order to acquire more comprehensive understanding of textile composites, the processing-microstructure-performance relationships for a variety of material systems, reinforcing schemes and processing technologies should be established. In this paper, emphasis is placed on the integrated analysis of three-dimensional (3-D) 2-step braided composites. The analysis includes the geometric model of unit cells, identification of key process parameters and processing windows due to limiting geometries of yarn jamming, and prediction of elastic constants of the composite. The coordinate transformation and averaging of stiffness and compliance constants are utilized in the prediction of elastic constants. Since there are several types of unit cells in the thickness and width directions of the composites, characterization of mechanical properties is based upon the macro-cell, which occupies the entire cross-section and the unit pitch length of the sample. The performance map demonstrates that a wide range of elastic properties can be achieved by varying the geometric and process parameters.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.983-984
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• 2008

This paper proposes a variable break that can offset prediction error as well as a pre-selection methods, based on the variable break, for enhanced unit selection. In Japanese, a sentence consists of several APs (Accentual phrases) and MPs (Major phrases), and the breaks between these phrases must predicted to realize text-to-speech systems. An MP also consists of several APs and plays a decisive role in making synthetic speech natural and understandable because short pauses appear at its boundary. The variable break is defined as a break that is able to change easily from an AP to an MP boundary, or from an MP to an AP boundary. Using CART (Classification and Regression Trees), the variable break is modeled stochastically, and then we pre-select candidate units in the unit-selection process. As the experimental results show, it was possible to complement a break prediction error and improve the naturalness of synthetic speech.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.354-362
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• 2012

Recently, High Efficiency Video Coding (HEVC) is under development jointly by MPEG and ITU-T for the next international video coding standard. Compared to the previous standards, HEVC supports variety of splitting units, such as coding unit (CU), prediction unit (PU), and transform unit (TU). Among them, it has been known that the recursive quadtree structure of CU can improve the coding efficiency while the encoding complexity is increased significantly. In this paper, a simple conditional probability to predict the early termination condition of recursive unit structure is introduced. The proposed conditional probability is estimated based on Bayes' formula from local statistics of rate-distortion costs in encoder. Experimental results show that the proposed method can reduce the total encoding time by about 32% according to the test configuration while the coding efficiency loss is 0.4%-0.5%. In addition, the encoding time can be reduced by 50% with 0.9% coding efficiency loss when the proposed method was used jointly with HM4.0 early CU termination algorithm.

• Journal of the Korea Concrete Institute
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• v.14 no.4
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• pp.457-466
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• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1283-1298
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• 2018

KOPRI(Korea Polar Research Institute) have researhed Arctic sea ice by using satellite remote sensing data since 2017 as a mission of KOPRI. The title of the reseach is "Development of Satellite Observation and Analysis for Arctc sea-ice". This project has three major aims; 1) development of prototype satellite data archive/manage system for Arctic sea ice monitoring, 2) development of sea ice remote sensing data processing and analysis technique, and 3) development of international satellite observing network for Arcitc. This reseach will give us that 1) deveolpment of sea ice observing system for northern sea route, 2) development of optimal remote sensing data processing technique for sea ice and selected satelite sensors, 3) development of international satellite onbservation network. I hope that this letter of introducton KOPRI satellite program for Arctic will help to understand Arctic remote sensing and will introduce you to step into the Arctic remote sensing, which Iis like a blue ocean of remote sensing.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.3
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• pp.265-274
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• 2011

This paper presents current state of the prediction simulator of structural characteristics of machinery equipment accuracy. Developed accuracy prediction simulator proceeds and estimates the structural analysis between the designer and simulator through the internet for convenience of designer. 3D CAD model which is input to the accuracy prediction simulator would simplified by the process of removing the small hole, fillet and chamfer. And the structural surface joints would be presented as the spring elements and damping elements for the structural analysis. The structural analysis of machinery equipment joints, containing rotary motion unit, linear motion unit, mounting device and bolted joint, are presented using Finite Element Method and their experiment. Finally, a general method is presented to tune the static stiffness at a rotation joint considering the whole machinery equipment system by interactive use of Finite Element Method and static load experiment.

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.13-20
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• 2013

Construction industry has become a larger and highly competitive industry. A successful construction project cannot be achieved only by efficient and fast construction techniques but also reasonable material cost and adequate transferring time of materials to installation. The steel industry in East Asia has become the mainstream in overall steel industries in over the world during the middle of the 21st century. China, Japan and Korea has been the main exportation countries. However, even though the international economic failure, China has increased the exportation amount and became an only exporting country which must be considered a serious problem regarding competitiveness in the international steel exportation industry. Thus, this study analyses the factors affecting the supply and demand amount of reinforced bars in the domestic field and moreover suggesting a unit cost prediction model using the System Dynamics simulation methodology, one of powerful prediction tools using cause-effect relationships. It is expected that this study contributes to the domestic steel industry growth in competitiveness in the international industry. In addition, the methodology used in this paper presents the frameworks for appropriate tools for market trend analysis and prediction of other markets.

• Journal of Ocean Engineering and Technology
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• v.38 no.1
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.