• Atmosphere
• /
• v.21 no.2
• /
• pp.209-220
• /
• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

• Transactions of Materials Processing
• /
• v.7 no.3
• /
• pp.197-206
• /
• 1998

This paper presents the prediction of dynamic recrystallization behavior during hot forging of Inconel 718. Another experiment of pancake forging was also carried out to examine the recrystallization ration dynamically recrystallizaed grain size, and grain growth in the forging. In experiments cylindrical billets were forged by two operations with variations of forging temperature, reduction ration of deformation. and preheating process at each forging step. Also the finite element program, developed here for the prediction using the metallurgical models was used for the analysis of to Inconel 718 upsetting and the results were compared with experimental ones.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.213-222
• /
• 2002

The 3-hour-interval prediction of ground-level temperature up to +45 hours in Seoul area is performed using dynamic linear models(DLM). Numerical outputs and observations we used as input values of DLM. According to compare DLM forecasts to RDAPS forecasts using RMSE, DLM improve the accuracy of prediction and systematic error of numerical model outputs are eliminated by DLM.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.1
• /
• pp.39-42
• /
• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Computers and Concrete
• /
• v.20 no.1
• /
• pp.49-56
• /
• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

• Journal of the Korean Society of Safety
• /
• v.18 no.1
• /
• pp.81-88
• /
• 2003

Recently, pile foundations were constructed in rough or soft ground than ground of well condition thus it is important that prediction of ultimate bearing capacity and calculation of proper safety factor applied pile foundation design. This study were performed to dynamic loading tests for the thirty two piles at four different construction sites and selected pile at three site were performed to static loading tests and then compare with measured value and value of static and dynamic loading tests. The load-settlement curve form the dynamic loading tests by CAPWAP was very similar to the results obtained from the static load tests. Based on dynamic and static loading tests, the reliability of pile-driving formula were analyzed and then suggested with proper safety factor for prediction of allowable bearing capacity in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1883-1888
• /
• 2000

In this paper a computer aided analysis method is proposed for durability assessment in the early design stages using dynamic analysis, stress analysis and fatigue life prediction method. From dynamic analysis of a vehicle suspension system, dynamic load time histories of a suspension component are calculated. From the dynamic load time histories and the stress of the suspension component, a dynamic stress time history at the critical location is produced using the superposition principle. Using linear damage law and cycle counting method, fatigue life cycle is calculated. The predicted fatigue life cycle is verified by experimental durability tests.

• Geomechanics and Engineering
• /
• v.37 no.3
• /
• pp.253-262
• /
• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.8
• /
• pp.741-751
• /
• 2000

To achieve high performance by exploiting instruction level parallelism aggressively in superscalar processors, it is necessary to overcome the limitation imposed by control dependences and data dependences which prevent instructions from executing parallel. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively its data dependent instruction based on the predicted outcome. In this paper, a hybrid value prediction scheme with dynamic classification mechanism is proposed. We design a hybrid predictor by combining the last predictor, a stride predictor and a two-level predictor. The choice of a predictor for each instruction is determined by a dynamic classification mechanism. This makes each predictor utilized more efficiently than the hybrid predictor without dynamic classification mechanism. To show performance improvements of our scheme, we simulate the SPECint95 benchmark set by using execution-driven simulator. The results show that our scheme effect reduce of 45% hardware cost and 16% prediction accuracy improvements comparing with the conventional hybrid prediction scheme and two-level value prediction scheme.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.6
• /
• pp.323-330
• /
• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.