• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.15-16
• /
• 2006

In-process surface roughness prediction is studied in this research. To implement in-process prediction, spindle displacement is introduced. Machined surface's roughness is assumed to be expressed in terms of spindle displacement. In-process measurement of spindle displacement is conducted using CCDS (cylindrical capacitive displacement sensor). Two prediction models are developed. One is simple linear model between measured surface roughness and values by spindle displacement. The other is multiple regression model including machining parameters like spindle speed, fee rate and radial depth of cut. Relation between machined surface roughness and roughness by spindle displacement are verified.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.5
• /
• pp.917-923
• /
• 2010

The local area weather information is very important element to estimate where the air-pollutant will flow. But the existing NBC hazard prediction model does not consider the local area weather information. So, in this paper, we present SN-HPM that uses the local area wether information to perform more accurate and reliable estimate, and embody it to program.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.749-752
• /
• 2005

In this paper, we introduce an Intersection Collision Avoidance (ICA) system as a convergence example of Telematics and USN technology and show several requirements for the ICA system. Also, we propose a system design that satisfies the requirements of reliable vehicular data acquisition, real-time data transmission, and effective intersection collision prediction. The ICA system consists of vehicles, sensor nodes and a base station that can provide drivers with a reliable ICA service. Then, we propose several technological solutions needed when implementing the ICA system. Those are about sensor nodes deployment, vehicular information transmission, vehicular location data acquisition, and intersection collision prediction methods. We expect this system will be a good case study applied to real Telematics application based on USN technology.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.03a
• /
• pp.172-177
• /
• 1998

An experimental study on the prediction of grinding wheel wear by dual detection methods was conducted by the laser displacement and acoustic emission(AE) system. The laser displacement sensor was located above the head of the grinding wheel and the AE sensor was set under the workpiece, where the wheel were condition can be detected. It was found that the dual detection methods by laser displacement system and AE system made it possible to predict the wheel wear. From the experiments, the root mean square(RMS) values both methods was found to be proportional to the grinding wheel wear.

• Korean Journal of Applied Biomechanics
• /
• v.32 no.1
• /
• pp.1-8
• /
• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.450-453
• /
• 2005

A forest fire occurs mainly as natural factor such as wind, temperature or human factor such as light. Recently, the most of forest fire prevention is prediction or prevision against forest fire by using remote sensing technology. However in order to forest fire prevention, the remote sensing has many limitations such as high cost and advanced technologies and so on. Therefore, we need to multisensor integration system that utilize not only remote sensing but also in-situ sensing in order to reduce large damage of forest fire though analysis of happen cause and prediction routing of occurred forest fire. In this paper we propose a multisensor integration system that offers prediction information of factors and route of forest fire by integrates collected data from remote sensor and in-situ sensor for forest fire prevention. The proposed system is based on wireless sensor network for collect observed data from various sensors. The proposed system not only offers great quality information because firstly, raw data level fuse different format of collected data from remote and in-situ sensor but also accomplish information level fusion based on result of first stage. Offered information from our system can help early prevention of factor and early prevision against occurred forest fire which transfer to SMS service or alert service into monitoring interface of administrator.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.186-188
• /
• 2005

Because forest fire changes the direction according to the environmental elements, it is difficult to predict the direction of it. Currently, though some researchers have been studied to which predict the forest fire occurrence and the direction of it, using the remote detection technique, it is not enough and efficient. And recently because of the development of the sensor technique, a lot of In-Situ sensors are being developed. These kinds of In-Situ sensor data are used to collect the environmental elements such as temperature, humidity, and the velocity of the wind. Accordingly we need the prediction technique about the environmental elements analysis and the direction of the forest fire, using the In-Situ sensor data. In this paper, as a technique for predicting the direction of the forest fire, we propose the correlation analysis technique about In-Situ sensor data such as temperature, humidity, the velocity of the wind. The proposed technique is based on the clustering method and clusters the In-Situ sensor data. And then it analyzes the correlation of the multivariate correlations among clusters. These kinds of prediction information not only helps to predict the direction of the forest fire, but also finds the solution after predicting the environmental elements of the forest fire. Accordingly, this technique is expected to reduce the damage by the forest fire which occurs frequently these days.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.7-14
• /
• 2022

Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.

• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.155-169
• /
• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.