• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.961-964
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• 1995

For geometric accuracy in the net shape machining, the problem of tool deflection should be resolved in some fashion. In particular, this is crucial in finish cut operation where slim tools are used. The purpose of this paper is to verify the validity and effectiveness of the prediction model of the machined surface. Experimental results are presented for the cut of steel material with HSS endmill of diameter 6mm on machining center. The results shows that 1) the machining error due totool deflection is serious even in the low cutting load, 2) by using the mechanistic simulation model with experimental coefficients, the machining error was predicted with maximum prediction error of 10% which was significantly reduced to the desired level by the path modification method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• 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.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.29-36
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• 2005

With the rapid growth of the Internet, the Internet-based robot has been realized by connecting off-line robot to the Internet. However, because the Internet is often irregular and unreliable, the varying time delay in data transmission is a significant problem for the construction of the Internet-based robot system. Thus, this paper is concerned with the development of an Internet-based robot system, which is insensitive to the Internet time delay. For this purpose, the PPS (Position Prediction Simulator) is suggested and implemented on the system. The PPS consists of two parts : the robot position prediction part and the projective virtual scene part. In the robot position prediction part, the robot position is predicted for more accurate operation of the mobile robot, based on the time at which the user's command reaches the robot system. The projective virtual scene part shows the 3D visual information of a remote site, which is obtained through image processing and position prediction. For the verification of this proposed PPS, the robot was moved to follow the planned path under the various network traffic conditions. The simulation and experimental results showed that the path error of the robot motion could be reduced using the developed PPS.

• 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.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.1-8
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• 2009

H.264/AVC video coding standard uses the rate distortion optimization method which determines the best coding mode for macroblock(MB) to improve coding efficiency. Whereas RDO selects the best coding mode, it causes the heavy computational burden comparing with previous standards. To reduce the complexity, in this paper, a fast intra prediction mode decision algorithm using directional gradients is proposed. The proposed algorithm is composed of 2-path structure. In the first path, $16{\times}16$ intra prediction mode is determined using directional gradients. In the second path, 3 modes instead of 9 modes are chosen for RDO to decide the best mode for $4{\times}4$ block. Finally, the two modes determined in the two-path decision process are compared to decide the final block mode. Experimental results show that the computation time of the proposed method is decreased to about 77% of the exhaustive mode decision method with negligible quality loss.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.131-140
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• 2020

The characteristics of ballistic missiles are changing rapidly but studies have mostly focused on fragmentary flight trajectory analysis estimating the changing characteristics of some types, while there is a lack of research on comprehensive and efficient ballistic search, detection and prediction for missiles including the new types that have been gaining attention lately. This paper analyzes the flight trajectory characteristics of ballistic missiles at various ranges considering flight path angle adjustment, specific impulse and drag force with altitude based on the optimized equations of motion reflecting the parameters of North Korea's general and new types of ballistic missiles. The flight trajectory characteristics of representative ranges for each ballistic missile were analyzed by adjusting the flight path angle in the minimum energy method, lofted method, and depressed method. In addition, High value target can attacked by ballistic missiles considering flight path angle adjustment at various points. It's expected to be used to Threat Evaluation and Weapon Allocation, and deployment of defense systems by interpreting the analysis of the latest Iskander-class ballistic missiles and the new multiple rocket launcher.

• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.81-89
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• 2008

Improved EKF suggests variable path prediction to reduce the event traffic caused by the information sharing among multi-users in networked virtual environment. The three dimensional virtual space is maintained consistently by endless status information exchange among dispersed users, and periodic status transmission brings traffic overhead in network. By using the error between the measured movement trace of dynamic information and the EKF predicted, we propose the method applied to predict the mobile packet of dynamic data which is simultaneously changing. And, the simulation results of DIS dead reckoning algorithms and EKF path prediction is compared here. It followed the specific path and while moving, the proposed method which it proposes predicting with DIS dead reckoning algorithm and to compare to the mobile path of the actual object and it got near it predicts the possibility of knowing it was.