• Journal of Magnetics
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• v.2 no.3
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• pp.101-104
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• 1997

Vector Moving Preisach model has been applied to the unoriented Co-based alloy thin film media. In the model, the out-of plane easy axis distribution of the particles was derived directly from the texture coefficient phkl obtained from XRD analysis, which corresponds to the fraction of the grains that have the {hkl} plane lying parallel to in-plane direction. The model was validated, by its prediction of a variety of responses, including major loop, minor loop, and the angular dependence of coercivities.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.32-42
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• 2013

Typical underwater piezoelectric spherical sensors are omni-directional, thus can measure the scalar quantity sound-pressure-magnitude only with the limitation not being able to measure the direction of the incoming wave. This paper proposes a method to simultaneously measure both the magnitude and direction of the sound wave with the spherical sensor. The method divides the piezoceramic sphere of the sensor into eight elements, and distinguishes the magnitude and direction of the sound pressure by combining the output voltage of the elements in a particular manner. Further, through the analysis of the sensitivity variation in relation to the structural parameters like radius and thickness of the piezoceramic sphere, we have suggested the way to improve the sensitivity of the vector sensor.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.179-189
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• 2001

This paper aims at faster processing and retrieval in fingerprint identification systems by reducing the amount of preprocessing and the size of the feature vector. The distribution of fingerprint directions is a set of local directions of ridges and furrows in small overlapped blocks in a fingerprint image. It is extracted initially as a set of 8-direction components through the Gabor filter bank. The discontinuous distribution of directions is smoothed to a continuous one and visualized as a direction image. Then the center of the distribution is selected as a reference point. A feature vector is composed of 192 sine values of the ridge angles at 32-equiangular positions with 6 different distances from the reference point in the direction image. Experiments show that the proposed algorithm performs the same level of correct identification as a conventional algorithm does, while speeding up the overall processing significantly by reducing the length of the feature vector.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.287-295
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• 2022

The GNSS coordinate time series is used as important data for geophysical analysis such as terrestrial reference frame establishment, crustal deformation, Earth orientation parameter estimation, etc. However, various factors may cause discontinuity in the coordinate time series, which may lead to errors in the interpretation. In this paper, we describe the discontinuity in the coordinate time series due to the equipment replacement for domestic GNSS stations and discuss the change in movement magnitude and velocity vector difference in each direction before and after discontinuity correction. To do this, we used three years (2017-2019) of data from 40 GNSS stations. The average magnitude of the velocity vector in the north-south, east-west, and vertical directions before correction is -12.9±1.5, 28.0±1.9, and 4.2±7.6 mm/yr, respectively. After correction, the average moving speed in each direction was -13.0±1.0, 28.2±0.8, and 0.7±2.1 mm/yr, respectively. The average magnitudes of the horizontal GNSS velocity vectors before and after discontinuous correction was similar, but the deviation in movement size of stations decreased after correction. After equipment replacement, the change in the vertical movement occurred more than the horizontal movement variation. Moreover, the change in the magnitude of movement in each direction may also cause a change in the velocity vector, which may lead to errors in geophysical analysis.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.475-487
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• 2010

Sea surface wind field was retrieved from high-resolution SIR-C SAR data by using CMOD algorithms off the east coast of Korea. In order to extract wind direction information from SAR data, a two-dimensional spectral analysis method was applied to the normalized radar cross section of the image. An $180^{\circ}$-ambiguity problem in the determination of wind direction was solved by selecting a direction nearest to the wind vector of the ECMWF reanalysis data. Comparison of the wind retrieval patterns with the ECMWF and NCEP/NCAR dataset showed RMS errors in the range of 1.30 to $1.72\;ms^{-1}$. In contrast, comparison of wind directions revealed large errors of greater than $60^{\circ}$, which is enormously higher than the permitted limit of about $20^{\circ}$ for satellite scatterometer winds. Compared with wind speed results from different algorithms, wind vectors based on commonly-used CMOD4 algorithm showed good agreement with those derived by other algorithms such as CMOD_IFR2 and CMOD5, particularly at medium winds from 4 to $8\;ms^{-1}$. However, apparent discrepancy appeared at low winds (< $4\;ms^{-1}$). This study also addressed an importance of accurate wind direction data to improve the accuracy of wind speed retrieval and discussed potential causes of wind retrieval errors from SAR data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.715-723
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• 2015

In this paper, we propose a context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm in Mobile Ad Hoc Networks. The existing algorithm in MANET, it has a vulnerability that the dynamic network topology and the absence of network expandability of mobility of nodes. The proposed algorithm performs cluster formation using a range of direction and threshold of velocity for the base-station, we calculate the exchange of the cluster head node probability using the direction and velocity for maintaining cluster formation. The DDV algorithm forms a cluster based on the cluster head node. As a result of simulation, our scheme could maintain the proper number of cluster and cluster members regardless of topology changes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2699-2707
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• 2013

We proposed the context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm at Mobile Ad-hoc Network(MANET). MANET has problem about dynamic topology, the lack of scalability of the network by mobile of node. By mobile of node, energy consumption rate is different. So it is important choosing routing algorithms for the minium of energy consumption rate. DDV-hop algorithms considers of the attribute of mobile node, create a cluster and maintain. And it provides a path by searching a route more energy efficient. We apply mobile of node by direction and time, the alogorighm of routning path and energy efficiency clustering is provided, it is shown the result of enery consumption that is optimized for the network.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.471-483
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• 1998

The influence of observations on the misclassification probability in multiple discriminant analysis under the equal covariance assumption is investigated by the local influence method. Under an appropriate perturbation we can get information about influential observations and outliers by studying the curvatures and the associated direction vectors of the perturbation-formed surface of the misclassification probability. We show that the influence function method gives essentially the same information as the direction vector of the maximum slope. An illustrative example is given for the effectiveness of the local influence method.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.158-164
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• 2017

Typical hydrophones in line array sensors for early detection of covert underwater targets can measure only sound-pressure-magnitude with the limitation of being unable to identify the direction of an incoming wave. In this study, a thickness shear mode vibrator was proposed as the main component of an inertia type vector hydrophone to measure both magnitude and direction of acoustic signals from targets. The equation to analyze the output voltage of the vibrator to an external force was derived, and the validity of the equation was verified through finite element analysis of a PMN-PT single crystal vibrator. The analysis results from this study will be utilized in the future for the design of inertia type vector hydrophones made of thickness shear vibrators.