• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.101-107
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• 2008

Traffic speed data may be missed due to detector malfunction or network problems. In this paper we have proposed effective methods to estimate the data which could not be collected through loop detectors. Our proposed algorithm has three steps. First step is to find the most similar neighbor data record by coefficient of correlation. Second step is to make some data records which is calculated by the 5 kinds of estimation methods. Third step is to compare the data records with history data record of observation link and thus the best method is selected. The proposed method is useful for estimating travel time.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.23-33
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• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.375-378
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• 2007

This paper describes indoor location estimation intelligent robot. It is loaded indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks. Spartan III(Xilinx, U.S.A.) is used as a main control device in the mobile robot and the current direction data is collected in the indoor location estimation system. The data is transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.544-549
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• 2007

Carrier-to-noise and interference ratio (CNIR) measurement is important in providing accurate channel quality measurements for orthogonal frequency division multiplexing (OFDM) systems in different bit loading environments. Interference and noise power estimation for CNIR measurement nay be carried out either with known data or without any prior knowledge about the transmitted data. With prior knowledge on transmitted data, better estimation can be achieved. However, this results in huge increase in system load. As for estimation methods without any prior knowledge, poor system performance results under certain circumstances. In this paper, we investigate the effect due to different interference and noise power on decision-directed (DD) CNIR estimation for OFDM systems and propose a new CNIR estimation scheme that provides optimal trade off between the performance and system load.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1195-1200
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• 2007

This paper describes indoor location estimation intelligent robot. Indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks were implemented in the robot. Spartan III(Xilinx, U.S.A.) was used as a main control device in the mobile robot and the current direction data was collected in the indoor location estimation system. The data was transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.296-302
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• 2022

This paper proposes a center of plantar foot pressure (CoP) trajectory estimation method based on Gaussian process regression, with the aim to show robust results regardless of the regions and numbers of FSRs of the insole sensor. This method can bring an interpolation between the measurement points inside the wearable insole sensor, and two experiments are conducted for performance evaluation. For this purpose, the input data used in the experiment are generated in three types (13 FSRs, 8 FSRs, 5 FSRs) according to the regions and numbers of FSRs. First, the estimation results of the CoP trajectory are compared using Gaussian process regression and weighted mean. As a result of each method, the estimation results of the two methods were similar in the case of 13 FSRs data. On the other hand, in the case of the 8 and 5 FSRs data, the weighted mean varies depending on the regions and numbers of FSRs, but the estimation results of Gaussian process regression showed similar results in spite of reducing the regions and numbers. Second, the estimation results of the CoP trajectory based on Gaussian process regression during several gait cycles are analyzed. In five gait cycles, the previous cycle and the current estimation results are compared, and it was confirmed that similar trajectories appeared in all. In this way, the method of estimating the CoP trajectory based on Gaussian process regression showed robust results, and stability was confirmed by yielding similar results in several gait cycles.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.45-54
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• 2022

Image depth estimation is a technology that is the basis of various image analysis. As analysis methods using deep learning models emerge, studies using deep learning in image depth estimation are being actively conducted. Currently, most deep learning-based depth estimation models are being trained with clean and ideal images. However, due to the lack of data on adverse conditions such as haze or fog, the depth estimation may not work well in such an environment. It is hard to sufficiently secure an image in these environments, and in particular, obtaining non-homogeneous haze data is a very difficult problem. In order to solve this problem, in this study, we propose a method of synthesizing non-homogeneous haze images and a learning method for a monocular depth estimation deep learning model using this method. Considering that haze mainly occurs outdoors, datasets mainly containing outdoor images are constructed. Experiment results show that the model with the proposed method is good at estimating depth in both synthesized and real haze data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.4
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• pp.84-94
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• 2023

The wave height and period regression curve is widely used to estimate the design wave period. In this study, the parameters of the curves are estimated, compared, and evaluated using the linear, robust linear, and nonlinear regression methods, respectively. The data used in the design wave height estimation are the annual maxima (AM) wave height and period data sets divided by typhoon and non-typhoon conditions, provided by the Ministry of Oceans and Fisheries (2019). The estimation parameters show significant differences in the local coastal waters and the estimation methods. The estimation parameters based on the Suh et al. (2008, 2010) method show the apparent bias, under-estimation in the intercept (scale) parameter, and over-estimation in the slope (exponent) parameter, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.517-524
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• 2022

Human pose estimation is a method of estimating a posture by extracting a human joint key point. When occlusion occurs, the joint key point extraction performance is lowered because the human joint is covered. The occlusion phenomenon is largely divided into three types of actions: self-contained, covered by other objects, and covered by background. In this paper, we propose an effective posture estimation method using a masking phenomenon enhancement technique. Although the posture estimation method has been continuously studied, research on the occlusion phenomenon of the posture estimation method is relatively insufficient. To solve this problem, the author proposes a data augmentation technique that intentionally masks human joints. The experimental results in this paper show that the intentional use of the blocking phenomenon enhancement technique is strong against the blocking phenomenon and the performance is increased.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.