• Journal of Korean Institute of Industrial Engineers
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• v.31 no.4
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• pp.334-340
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• 2005

A mouse is an important input device that is used in most of all computer works. A mouse control time prediction model was proposed in this study. Especially, the model described the time of mouse control that made a cursor to move within path constraints. The model was developed by a laboratory experiment. Cursor movement times were measured in 36 task conditions; 3 levels of path length, 3 levels of path width and 4 levels of target's width. 12 subjects participated in all conditions. The time of cursor movement with path constraints could be better explained by the combination of Fitts' law with steering law($r^2=0.947$) than by the other models; Fitts' law($r^2=0.740$), Steering law($r^2=0.633$) and Crossman's model($r^2=0.897$). The proposed model is expected to be used in menu design or computer game design.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.579-584
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• 2003

All of propagation path loss prediction models, which have been presented up to date, are oかy for ground living space. In reality, sea surface free space is different from ground living space in physical hierarchical structure. If the propagation path prediction model for ground living space is applied to the sea surface free space, propagation path loss will be smaller than actual value, while the maximum service straight line will become shorter. Thus this paper proposed and simulated the propagation path loss prediction model for predicting propagation path loss more accurately in sea surface free space, with its focus on CDMA mobile communication frequency band. Then the simulation results were compared to actual survey to verify its practicality.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.29-38
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• 2019

A path prediction method using lifelog requires a large amount of training data for accurate path prediction, and the path prediction performance is degraded when the training data is insufficient. The lack of training data can be solved using data of other users having similar user movement patterns. Therefore, this paper proposes a path prediction algorithm based on user similarity. The proposed algorithm learns the path in a triple grid pattern and measures the similarity between users using the cosine similarity technique. Then, it predicts the path with applying measured similarity to the learned model. For the evaluation, we measure and compare the path prediction accuracy of proposed method with the existing algorithms. As a result, the proposed method has 66.6% accuracy, and it is evaluated that its accuracy is 1.8% higher than other methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.24 no.1
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• pp.9-17
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• 2020

The stochastic point-source model has been widely used in generating artificial ground motions, which can be used to develop a ground motion prediction equation and to evaluate the seismic risk of structures. This model mainly consists of three different functions representing source, path, and site effects. The path effect is used to emulate decay in ground motion in accordance with distance from the source. In the stochastic point-source model, the path attenuation effect is taken into account by using the geometrical attenuation effect and the inelastic attenuation effect. The aim of this study is to develop accurate equations of ground motion attenuation in the Korean peninsula. In this study, attenuation was estimated and validated by using a stochastic point source model and observed ground motion recordings for the Korean peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4665-4683
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• 2019

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.33-42
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• 2003

Kuo et al proposed an on-line method for an adaptive prediction error filter for improving secondary path modeling performance in the modeling method of the secondary path. This method have some disadvantages, namely having to use additive noise with the result that noise control performance is not good since it is focused on the estimated performance of the secondary path. In this paper, we proposes a modified Kuo model using gain control parameter and delay. It uses a reference signal for additive noise to improve the problems in the existing Kuo model.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1038-1043
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• 2002

The demand for multimedia service using Ka-band satellite communication are growing rapi이y. So, in this paper, we have analyzed rain attenuation with typical model, and proposed prediction model of rain attenuation in high frequency(over 20[GHz]). Path loss model by rain attenuation is based upon rain rate of representative region(6 cities). Proposed prediction model of rain attenuation and parameter of satellite link can be available for the Ka-band satellite communication.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.8 s.99
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• pp.848-854
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• 2005

The attenuation due to rain has been recognized as one of the major causes of unavailability of radio communication systems operating above about 10 GHz. To design radio links for telecommunications and to evaluate attenuation due to rainfall, it is important to have a good prediction model for rain attenuation such as a model for drop-size distribution of rainfall(DSD), a theoretical model for specific rain attenuation, and an empirical model fur effective path length through rain. In this paper, the extended generalized gamma distribution for drop-size distribution, based on the measurements in Chnugnam National University, is proposed as a new DSD model, and predicted specific attenuation characteristics using proposed DSD model and rain attenuation values in the 44 GHz satellite path using ITU-R effective path length model, are analysed. The predicted attenuation levels are also compared. It is found that an accurate prediction method for DSD is very important to reduce the prediction error in the local satellite path.