• Current Optics and Photonics
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• v.5 no.6
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• pp.641-651
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• 2021

To handle the servo delay in a real-time adaptive optics system, a linear subspace system identification algorithm was employed to model the system, and the accuracy of the system identification was verified by numerical calculation. Experimental verification was conducted in a real test bed system. Through analysis and comparison of the experimental results, the convergence can be achieved only 200 times with prediction and 300 times without prediction. After the wavefront peak-to-valley value converges, its mean values are 0.27, 4.27, and 10.14 ㎛ when the communication distances are 1.2, 4.5, and 10.2 km, respectively. The prediction algorithm can effectively improve the convergence speed of the peak-to-valley value and improve the free-space optical communication performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.1013-1019
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• 2016

This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.31-43
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• 2008

Travel time prediction is an indispensable to many advanced traveler information systems(ATIS) and intelligent transportation systems(ITS). In this paper we propose a method to predict travel time using $Na{\ddot{i}}ve$ Bayesian classification method which has exhibited high accuracy and processing speed when applied to classily large amounts of data. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. For a given route, we consider time-varying average segment velocity to perform more accuracy of travel time prediction. We compare the proposed method with the existing prediction algorithms like link-based prediction algorithm [1] and Micro T* algorithm [2]. It is shown from the performance comparison that the proposed predictor can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.

• International Journal of Highway Engineering
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• v.16 no.2
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• pp.53-59
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• 2014

PURPOSES : This study estimated an asphalt pavement internal behavior under uphill lanes considering reducing speed of heavy truck on uphill slope. METHODS : Truck performance curve which has been adapted to "Korea Highway Capacity Manual" was analyzed. And asphalt pavement internal behaviors were estimated with Multi-layered elastic analysis using KPRP(Korea Pavement Research Program) dynamic modulus prediction equations. RESULTS : As a result, it is shown that when the standard truck drives 2.0 km at a speed of 80 km/h in 8% climbing slope, it's speed reduced to 25.4 km/h, at same time frequency in asphalt layer decrease to 67.2% and it's dynamic modulus degrades to 30.9%. Based on these results, internal behavior as decreasing vehicle speed on uphill lanes were estimated. CONCLUSIONS : From the results of Multi-layered elastic analysis, internal behavior showed that when the standard truck drives 2.0 km at a speed of 80 km/h in 8% slope on uphill lanes, vertical strain was increased to 44.4% at the bottom of surface course, and lateral tensile strain was increased to 20.5% at the bottom of base course.

• Korean Chemical Engineering Research
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• v.55 no.5
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• pp.668-678
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• 2017

Intermolecular interactions were studied for binary mixtures of 2-propanol + cyclohexane, n-hexane, benzene, toluene, o-, m- and p-xylenes by measuring ultrasonic speeds (u) over the entire range of composition at 298.15 K and 308.15 K. From these results the deviation in ultrasonic speed was calculated. These results were fitted to the Redlich-Kister equation to derive the binary coefficients along with standard deviations between the experimental and calculated data. Acoustic parameters such as excess isentropic compressibility ($K_s^E$), intermolecular free length ($L_f$) and available volume ($V_a$) were also derived from ultrasonic speed data and Jacobson's free length theory. The ultrasonic speed data were correlated by Nomoto's relation, Van Dael's mixing relation, impedance dependence relation, and Schaaff's collision factor theory. Van Dael's relation gives the best prediction of u in the binary mixtures containing aliphatic hydrocarbons. The ultrasonic speed data and isentropic compressibility were further analyzed in terms of Jacobson's free length theory.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.642-649
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• 2012

This paper deals with a copper die casting induction motor which has several advantages of motor performance. The developed motor is used as spindle motor in machining center. The dynamic characteristic analysis of rotor is dealt with for precision machining. The critical speed of rotor considering rotation and gyroscopic effect should be above operating speed, 18,000 rpm, and have a 201 % sufficient separation margin. Also, the 3-D unbalance vibration response analysis is performed and enabled the prediction of the expected vibration amplitude by unbalance in high speed. The unbalance vibration responses of each position on the rotor are satisfied with allowable vibration displacement of API 611 standard according to balancing G grade(G 0.4, G 2.5, G 6.3). Copper die casting high speed induction motor is successfully developed and verified by experiment.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.61-67
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• 2013

In this paper, it was studied how the critical speed which could occur hot judder due to disk temperature. Through the dynamometer experiment, we measured the critical velocity and surface temperature when the hot judder occur on the disk break. Also with the critical velocity theory equation and the temperature change graph of factors which used in the equation, we was induced experiment equation including theory equation and experiment values. And it has compared with the method which approach as linea. From this, we predicted the change of critical speed which could occur hot judder due to disk temperature. In addition, critical speed graph has compared with actual driving speed and disc temperature at a vehicle test. Therefore it was estimate to possibility of arising hot judder.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.