• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.334-339
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• 2019

In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.542-553
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• 2005

Recommendation systems analyze user preferences and recommend items to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper we propose a method of integrating additional feature information of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first fill in unknown preference values by using the probability distribution of feature values, then generate the top-N recommendations by applying collaborative filtering on the modified data. We call this method of filling unknown preference values as data blurring. Several experimental results that show the effectiveness of the proposed method are also presented.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.691-702
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• 2013

In this study the quantitative outlier-filtering algorithm has been developed using the smoothing method based on the day-of-the-week traffic volume variation pattern and then, in order to test the effectiveness of the algorithm, it has been used to identify outliers from the traffic volume data collected at 14 continuous traffic counts sites on the national highways in the year 2010. The test results are satisfactory since the filtering rate is 98.2% for normal days and the mis-filtering rate is 8.0% for abnormal days. Therefore, the algorithm will be able to be used for roughly-but-quickly filtering outliers from the collected traffic volume data.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.177-191
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.532-535
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• 2005

These days, many image processing techniques have been studied for effective image compression. Among those, 2D image filtering is widely used for 2D image processing. The 2D image filtering can be implemented by performing ID linear filtering separately in the direction of horizontal and vertical. Efficiency of image compression depends on what filtering method is used. Generally, circular convolution is widely used in the 2D image filtering for image processing. However it doesn't consider correlations at the region of image boundary, therefore filtering can not be performed effectively. To solve this problem. I proposed new convolution technique using Symmetric-Mirroring convolution, satisfying the 'alias-free' and 'error-free' requirement in the reconstructed image. This method could provide more effective performance than former compression methods. Because it used very high correlative data when performed at the boundary region. In this paper, pre-processing filtering in H.264 codec was adopted to analyze efficiency of proposed filtering technique, and the simulator developed by Matlab language was used to examine the performance of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.26-33
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• 1992

This paper presents lattice structure of bilinear filter and the conversion equations from lattice parameters to direct-form parameters. Billnear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately, and usually with considerable parsimony in the number of coefficients required. The lattice filter formulation transforms the nonlinear filtering problem into an equivalent multichannel linear filtering problem and then uses multichannel lattice filtering algorithms to solve the nonlinear filtering problem. The lattice filters perform a Gram-Schmidt orthogonalization of the input data and have very good easily extended to more general nonlinear output feedback structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.75-81
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• 2012

With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user's preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.529-538
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• 2012

By using an algorithm derived by a multiple linear regression analysis, a technique for filtering was developed; and by using the developed technique, the results of conducting filtering of the raw data collected via scanning with a terrestrial LiDAR the actual sloped terrain was analyzed. As such, when filtering was applied by dividing the observation areas into two areas with the topographical line as a reference in order to improve the filtering accuracy, it was seen that the filtering accuracy improved by about 8.73% as compared to when filtering was applied without dividing the observation area. In addition, considering the fact that the accuracy improved by 5~7% when the sloped sides of a multicurvature topography were divided and a complex filtering applied as compared to when filtering was applied for the entire area or by regions, it can be asserted that the accuracy was higher when a complex filtering was conducted by dividing the sloped areas where the slope is not constant due to the multi-curvature of topography.

• The Journal of Bigdata
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• v.2 no.1
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• pp.41-48
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• 2017

Recently, the popularity of the recommendation system and the evaluation of the performance of the algorithm of the recommendation system have become important. In this study, we used modeling and RMSE to verify the effectiveness of various algorithms in movie data. The data of this study is based on user-based collaborative filtering using Pearson correlation coefficient, item-based collaborative filtering using cosine correlation coefficient, and item-based collaborative filtering model using singular value decomposition. As a result of evaluating the scores with three recommendation models, we found that item-based collaborative filtering accuracy is much higher than user-based collaborative filtering, and it is found that matrix recommendation is better when using matrix decomposition.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.19-27
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• 2023

In the process of collecting social reviews, a number of noise reviews irrelevant to a given search keyword can be included in the search results. To filter out such reviews, machine learning can be used. However, if the number of reviews is insufficient for a target place to be analyzed, filtering accuracy can be degraded due to the lack of training data. To resolve this issue, we propose a supervised learning method to improve accuracy of the noise review filtering for the places with insufficient reviews. In the proposed method, training is not performed by an individual place, but by a group including several places with similar characteristics. The classifier obtained through the training can be used for the noise review filtering of an arbitrary place belonging to the group, so the problem of insufficient training data can be resolved. To verify the proposed method, a noise review filtering model was implemented using LSTM and BERT, and filtering accuracy was checked through experiments using real data collected online. The experimental results show that the accuracy of the proposed method was 92.4% on the average, and it provided 87.5% accuracy when targeting places with less than 100 reviews.