• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.428-432
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• 2007

We present a novel method for determining k nearest neighbors, which accurately recognizes the underlying clusters in a data set. To this end, we introduce the "tiling neighborhood" which is constructed by tiling a number of small local circles rather than a single circle, as existing neighborhood schemes do. Then we formulate the problem of determining the tiling neighborhood as a minimax optimization, leading to an efficient message passing algorithm. For several real data sets, our method outperformed the k-nearest neighbor method. The results suggest that our method can be an alternative to existing for general classification tasks, especially for data sets which have many missing values.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.223-224
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• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.887-890
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• 2007

For a integrated system to control several parking lots within a commercial district, it is required to guide a route to a nearest neighbor parking lot considering diverse realtime constraints such as realtime status of parking lots and changes of an access route. This paper proposes an optimized route-searching technique of integrated parking control system considering realtime contraints. In concrete, it proposes a method of researching a route in the surrounding area considering various status of parking lots that a customer designates (no parking, closed, under construction, no passing of a road) and deals with a route-searching technique optimized for each situation in detail.

• Journal of Korea Game Society
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• v.20 no.5
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• pp.53-64
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• 2020

Supporting NNS method in KD-Tree algorithm is essential in multidimensional data applications. In this paper, we propose HD-Tree, a high-performance Lock-Free KD-Tree that supports NNS in situations where reads and writes occurs concurrently. HD-Tree reduced the number of synchronization nodes used in NNS and requires less atomic operations during Lock-Free method execution. Comparing with existing algorithms, in a multi-core system with 8 core 16 thread, HD-Tree's performance has improved up to 95% on NNS and 15% on modifying in oversubscription situation.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.413-423
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• 2008

The template matching method is used as a simple method to track objects or patterns that we want to search for in the input image data from image sensors. It recognizes a segment with the highest correlation as a target. The concept of this method is similar to that of SNF (Strongest Neighbor Filter) that regards the measurement with the highest signal intensity as target-originated among other measurements. The SNF assumes that the strongest neighbor (SN) measurement in the validation gate originates from the target of interest and the SNF utilizes the SN in the update step of a standard Kalman filter (SKF). The SNF is widely used along with the nearest neighbor filter (NNF), due to computational simplicity in spite of its inconsistency of handling the SN as if it is the true target. Probabilistic Strongest Neighbor Filter for m validated measurements (PSNF-m) accounts for the probability that the SN in the validation gate originates from the target while the SNF assumes at any time that the SN measurement is target-originated. It is known that the PSNF-m is superior to the SNF in performance at a cost of increased computational load. In this paper, we suggest an image tracking algorithm that combines the template matching and the PSNF-m to estimate the states of a tracked target. Computer simulation results are included to demonstrate the performance of the proposed algorithm in comparison with other algorithms.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.283-291
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• 2016

As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.289-297
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• 2007

Color image scale captures the knowledge of colorists and represents both adjectives and colors in the same adjective image scales in order to select color(s) corresponding to an adjective. Due to the difficulty of psychological experiment and statistical analysis, in general, only a limited number of colors are located in the color image scales. This can make color selection process hard especially to non-expert. In this paper, we propose an interpolation of color image scale based on the fuzzy K-nearest neighbor method, which provides continuous colors according to the coordinates of the image scales. The experimental results show that the interpolated image scales can be practically useful for color selection process.