• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1486-1495
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• 2018

This paper investigates the autonomous ground vehicle (AGV) localization filter design problem under GNSS-denied environments. It is assumed that the given landmarks do not have unique features due to the lack of a prior knowledge on them. For such case, the AGV may have difficulties in distinguishing the position measurement of the detected landmark from those of other landmarks with the same feature, hence the conventional localization filters are not applicable. To resolve this technical issue, the localization filter design problem is formulated as a special form of the data association determining whether the detected feature is actually originated from which landmark. The measurement hypotheses generated by landmarks with the same feature are evaluated by the nearest neighbor data association scheme to reduce the computational burden. The position measurement corresponding to the landmark with the most probable hypothesis is used for localization filter. Through the experiments in real-driving condition, it is shown that the proposed method provides satisfactory localization performance in spite of using non-unique landmarks.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.125-127
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• 2004

High-resolution satellite images offer abundance information of the earth surface for remote sensing applications. The information includes geometry, texture and attribute characteristic. The pixel-based image classification can't satisfy high-resolution satellite image's classification precision and produce large data redundancy. Object-oriented information extraction not only depends on spectrum character, but also use geometry and structure information. It can provide an accessible and truly revolutionary approach. Using Beijing Spot 5 high-resolution image and object-oriented classification with the eCognition software, we accomplish the cultures' precise classification. The test areas have five culture types including water, vegetation, road, building and bare lands. We use nearest neighbor classification and appraise the overall classification accuracy. The average of five species reaches 0.90. All of maximum is 1. The standard deviation is less than 0.11. The overall accuracy can reach $95.47\%.$ This method offers a new technology for high-resolution satellite images' available applications in remote sensing culture classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.1-11
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• 2013

This study developed the algorithm of location-based service for supplying the efficient tourism service to traveller using mobile device and applied it to the Jeonju HANOK village. First, the location service was advanced using algorithm coupling with GPS error range and travel speed in single line, and with GPS location and nearest neighbor method to line in multiple one. Also this study developed a program using DuraMap-Xr spatial engine for establishing topology to Node and Link in line automatically. And the foundation was prepared for improving travel convenience by programming location-based service technology to single and multiple lines based on Blackpoint-Xr mobile application engine.

• Korean Journal of Materials Research
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• v.16 no.8
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• pp.473-478
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• 2006

The coherent interface energies and misfit strain energies of Fe/XN (X=Ti, Zr, Hf) systems were calculated by first principles method. The interface energies in Fe/TiN, Fe/ZrN and Fe/HfN systems were 0.343, 0.114, and 0.030 $J/m^2$, respectively. Influence of bond energy was estimated using the discrete lattice plane/nearest neighbor broken bond(DLP/NNBB) model. It was found that the dependence of interface energy on the type of nitride was closely related to changes of the bond energies between Fe, X and N atoms before and after formation of the Fe/XN interfaces. The misfit strain energies in Fe/TiN, Fe/ZrN, and Fe/HfN systems were 0.239, 1.229, and 0.955 eV per 16 atoms(Fe; 8 atoms and XN; 8 atoms). More misfit strain energy was generated as the difference of lattice parameters between the bulk Fe and the bulk XNs increased.

• Wind and Structures
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• v.23 no.1
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• pp.19-43
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• 2016

In the present study, a new methodology is presented to study the ride comfort and bridge responses of a long-span bridge-traffic-wind coupled vibration system considering stochastic characteristics of traffic flow and bridge surface progressive deterioration. A three-dimensional vehicle model with 24 degrees-of-freedoms (DOFs) including a three-dimensional non-linear suspension seat model and the longitudinal vibration of the vehicle is firstly presented to study the ride comfort. An improved cellular automaton (CA) model considering the influence of the next-nearest neighbor vehicles and a progressive deterioration model for bridge surface roughness are firstly introduced. Based on the equivalent dynamic vehicle model approach, the bridge-traffic-wind coupled equations are established by combining the equations of motion of both the bridge and vehicles in traffic using the displacement relationship and interaction force relationship at the patch contact. The numerical simulations show that the proposed method can simulate rationally the ride comfort and bridge responses of the bridge-traffic-wind coupled system; and the vertical, lateral, and longitudinal vibrations of the driver seat model can affect significantly the driver's comfort, as expected.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.37-44
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• 2002

Automatic text classification is a task of assigning predefined categories to free text documents. Its importance is increased to organize and manage a huge amount of text data. There have been some researches on automatic text classification based on machine learning techniques. While most of them was focused on proposal of a new machine learning methods and cross evaluation between other systems, a through evaluation or optimization of a method has been rarely been done. In this paper, we propose an improving method of kNN-based Korean text classification system using heuristic informations about decision function, the number of nearest neighbor, and feature selection method. Experimental results showed that the system with similarity-weighted decision function, global method in considering neighbors, and DF/ICF feature selection was more accurate than simple kNN-based classifier. Also, we found out that the performance of the local method with well chosen k value was as high as that of the global method with much computational costs.

• Journal of Magnetics
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• v.13 no.4
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• pp.124-127
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• 2008

The magnetic and electronic properties of Ni impurity in bcc Fe ($Ni_1Fe_{26}$) are investigated using the full potential linearized augmented plane wave (FLAPW) method based the generalized gradient approximation (GGA). We found that the Ni impurity in bcc Fe increases both the lattice constant and the magnetic moment of bcc Fe. The calculated equilibrium lattice constant of $Ni_1Fe_{26}$ in the ferromagnetic state was 2.84 A, which is slightly larger than that of bcc Fe (2.83 ${\AA}$). The averaged magnetic moment per atom of $Ni_1Fe_{26}$ unit cell was calculated to be $2.24{\mu}_B$, which is greater than that of bcc Fe (2.17 ${\mu}_B$). The enhancement of magnetic moment of $Ni_1Fe_{26}$ is mainly contributed by the nearest neighbor Fe atom of Ni, i.e., Fe1, and this can be explained by the spin flip of Fe1 d states. The density of states shows that Ni impurity forms a virtual bound state (VBS), which is contributed by Ni $e_{g{\downarrow}}$ states. We suggest that the VBS caused by the Ni impurity is responsible for the spin flip of Fe1 d states.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.112-114
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• 2011

On multimedia databases, in order to realize the fast access method, indexing methods for the multidimension data space are used. However, since it is a premise to use the Euclid distance as the distance measure, this method lacks in flexibility. On the other hand, there are metric indexing methods which require only to satisfy distance axiom. Since metric indexing methods can also apply for distance measures other than the Euclid distance, these methods have high flexibility. This paper proposes an improved method of VP-tree which is one of the metric indexing methods. VP-tree follows the node which suits the search range from a route node at searching. And distances between a query and all objects linked from the leaf node which finally arrived are computed, and it investigates whether each object is contained in the search range. However, search speed will become slow if the number of distance calculations in a leaf node increases. Therefore, we paid attention to the candidates selection method using the triangular inequality in a leaf node. As the improved methods, we propose a method to use the nearest neighbor object point for the query as the datum point of the triangular inequality. It becomes possible to make the search range smaller and to cut down the number of times of distance calculation by these improved methods. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%~12% of search time of the traditional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.83-92
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• 2004

Gene expression profile is numerical data of gene expression level from organism measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify cancer with gene expression profile. Because not all the genes are related to classification, it is needed to select related genes that is called feature selection. This paper proposes a new gene selection method using forward selection method in regression analysis. This method reduces redundant information in the selected genes to have more efficient classification. We used k-nearest neighbor as a classifier and tested with colon cancer dataset. The results are compared with Pearson's coefficient and Spearman's coefficient methods and the proposed method showed better performance. It showed 90.3% accuracy in classification. The method also successfully applied to lymphoma cancer dataset.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.