• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.835-845
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• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• Journal of Korean Association for Spatial Structures
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• v.7 no.6
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• pp.75-82
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• 2007

In this study, a design-supporting system, which is intended to assist engineers in the schematic phase of the structural design, is developed using a case database that contains design information of tall buildings in Korea. A case-based reasoning method utilizing the case database is proposed. The inductive retrieval module for selecting structural system, in the initial stage, from the design information of case database for 47 tall buildings is presented. Also, the nearest-neighbor retrieval method for selecting similar design cases is introduced.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.569-577
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• 2016

Place recognition for LBS (Location Based Service) has been one of the important techniques for user-oriented service. FLANN (Fast Library for performing Approximate Nearest Neighbor) of place recognition with image features is fast, but it is affected much by environmental condition such as occlusions. This paper presents a place recognition method using quad vocabulary tree with SURF (Speeded Up Robust Features). In learning stage, an image is represented with spatial pyramid of three levels and vocabulary trees of their sub-regions are constructed. Query image is matched with the learned vocabulary trees in each level. The proposed method measures homography error of the matched features. By considering the number of inliers in sub-region, we can improve place recognition performance.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.418-421
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• 2007

In this paper, we proposed a hierarchical nearest-neighbor searching method for deciding fitness of a clustered segment. It is difficult to distinguish the difference between correct spots and atypical noisy spots in footprint patterns. Therefore we could not completely remove unsuitable noisy spots from binarized image in image preprocessing stage or clustering stage. As a preprocessing stage for recognition of insect footprints, this method decides whether a segment is suitable or not, using degree of clustered segment fitness, and then unsuitable segments are eliminated from patterns. Removing unsuitable segments can improve performance of feature extraction for recognition of inset footprints.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.912-915
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• 2002

This paper presents off-line handwritten numeral recognition method by using Eigen-Vectors. In this method, numeral features are extracted statistically by using Eigen-Vectors through KL transform and input numeral is recognized in the feature space by the nearest-neighbor classifier. In our feature extraction method, basis vectors which express best the property of each numeral type within the extensive database of sample numeral images are calculated, and the numeral features are obtained by using this basis vectors. Through the experiments with the unconstrained handwritten numeral database of Concordia University, we have achieved a recognition rate of 96.2%.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.6
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• pp.265-270
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• 2002

The determination of DNA hybridization reaction can apply the molecular biology research, clinic diagnostics, bioengineering, environment monitoring, food science and application area. So, the improvement of DNA hybridization detection method is very important for the determination of this hybridization reaction. Several molecular biological techniques require accurate predictions of matched versus mismatched hybridization thermodynamics, such as PCR, sequencing by hybridization, gene diagnostics and antisense oligonucleotide probes. In addition, recent developments of oligonucleotide chip arrays as means for biochemical assays and DNA sequencing requires accurate knowledge of hybridization thermodynamics and population ratios at matched and mismatched target sites. In this study, we report the characteristics of the probe and matched, mismatched target oligonucleotide hybridization reaction using thermodynamic method. Thermodynamic of 5 oligonucleotides with central and terminal mismatch sequences were obtained by measured UV-absorbance as a function of temperature. The data show that the nearest-neighbor base-pair model is adequate for predicting thermodynamics of oligonucleotides with average deviations for $\Delta$H$^{0}$ , $\Delta$S$^{0}$ , $\Delta$G$_{37}$ $^{0}$ and T$_{m}$, respectively.>$^{0}$ and T$_{m}$, respectively.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.429-440
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• 2005

As rapid improvement in electronic technologies makes sensor hardware more powerful and capable, the application range of sensor networks Is getting to be broader. The main purpose of sensor networks is to monitor the phenomena in interesting regions (e.g., factory warehouses, disaster areas, wild fields, etc) and return required data. The k Nearest Neighbor (KNN) query that finds k objects which are geographically close to the given point is an Important application in sensor networks. However, most previous approaches are either seem to be impractical or are not energy-efficient in resource-limited sensor networks. In this paper. we propose an efficient KNN query processing method in sensor networks. In the proposed method, we dynamically increase searching boundary, if necessary, and traverse nodes inside the boundary until finding k nearest neighbors. Since only the representative sensor nodes are visited, our algorithm reduces a number of messages. We show thorough experiments that the proposed method performs better than the existing method in various network environments.