• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.353-356
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• 2006

In this paper, the prediction method of internal temperature for reinforced concrete columns under high temperature is presented. Finite element method is employed to facilitate thermal analysis for any position of column. And the effect of the heat of vaporization is applied. To demonstrate the validity of this numerical procedure, the prediction by the proposed algorithm is compared with the test results from this study. The proposed algorithm is in good agreement with experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.39-42
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• 2005

H.264, a recently proposed international video coding standard, has adopted context-based adaptive variable length coding (CAVLC) as the entropy coding tool in the baseline profile. By combining an adaptive variable length coding technique with context modeling, we can achieve a high degree of redundancy reduction. However, CAVLC in H.264 has weakness that the correct prediction rate of the variable length coding (VLC) table is low in a complex area, such as the boundary of an object. In this paper, we propose a VLC table prediction scheme considering multiple reference blocks; the same position block of the previous frame and the neighboring blocks of the current frame. The proposed algorithm obtains the new weighting values considering correctness of the VLC table for each reference block. Using this method, we can enhance the prediction rate of the VLC table and reduce the bit-rate.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.98-109
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• 2006

Fire control system(FCS) errors can be classified as hardware errors, filter prediction errors, effective ballistic function errors, and aiming errors. Among these errors, the filter prediction errors are the most significant error sources. To reduce them, a target future position calculation method using the acceleration estimate is suggested and it is compared with the constant velocity target prediction method. Simulation results show that the suggested method has better performance than the constant velocity prediction method. Target tracking algorithm is established with multiple target tracking filters based on IMM structure.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.191-196
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• 2006

In GPS Positioning, the error of satellite orbit will affect user's position accuracy directly, it is important to determine the satellite orbit precise. The real-time orbit is needed in kinematic GPS positioning, the precise GPS orbit from IGS would be delayed long time, so orbit prediction is key to real-time kinematic positioning. We analyze the GPS predicted ephemeris, on the base of comparison of EKF and UKF, a new orbit prediction method is put forward based on UKF in this paper, the result shows that UKF improves the orbit predicted precision and stability. It offers a new method for others satellites orbit determination as Galileo, and so on.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of the Korean Society of Tobacco Science
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• v.11 no.2
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• pp.115-126
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• 1989

This study was conducted to determine the time and methods of predicting tobacco yield, by studying the relationship of yield components to yield. 1. The relationship between each position in leaf dry weight and approached gradually each other and also correlation coefficient of top leaf was higher than that of lower leaf. The leaf dry weight per plant was highly correlated with leaf area from 16th leaf position on stalk. Leaf dry weight of each leaf position on stalk was highly correlated with leaf dry weight per plant at 14 to 16th leaf position. 2. The correlation coefficient between leaf dry weight and leaf area per plant was higher at the late growth stage than at the early growth stage, and higher between the near stages. Correlation coefficient between leaf dry weights was higher than that of leaf areas. 3. Flue-cured tobacco yield be estimated from leaf dry weight per plant at 50 to 55 days after transplanting. 4. Air-cured tobacco yield could be predicted from leaf dry weight per plant at 60 days after transplanting.

• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.26-36
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• 2001

One of the most important problems in time-varying image sequences is the automatic target tracking. This paper proposes a position prediction and tracking technique of moving object using fuzzy prediction. First, the object is segmented from background of the image using accumulative difference image technique. Then centroid of the segmented object is extracted by using the centroid method, and we propose to apply variable size searching window to the object in order to increase the tracking performance. Also, non-linear prediction is required for efficient object tracking. Therefore, in this paper, fuzzy prediction method is proposed for predicting the location of the moving object at next frame. An experimental result shows that the proposed fuzzy prediction system tracks the moving object in stable under various conditions.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.277-284
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• 2016

A new prediction scheme has been proposed for the robust teleoperation in a non-visible environment. The positioning error caused by the time delay in the non-visible environment has been compensated for by the Smith predictor and the sensory data have been estimated by the Grey model. The Smith predictor is effective for the compensation of the positioning error caused by the time delay with a precise system model. Therefore the dynamic model of a mobile robot has been used in this research. To minimize the unstable and erroneous states caused by the time delay, the estimated sensor data have been sent to the operator. Through simulations, the possibility of compensating the errors caused by the time delay has been verified using the Smith predictor. Also the estimation reliability of the measurement data has been demonstrated. Robust teleoperations in a non-visible environment have been performed with a mobile robot to avoid the obstacles effective to go to the target position by the proposed prediction scheme which combines the Smith predictor and the Grey model. Even though the human operator is involved in the teleoperation loop, the compensation effects have been clearly demonstrated.