• Journal of Navigation and Port Research
• /
• v.27 no.4
• /
• pp.465-469
• /
• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas, However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. the other problem of MUSIC and ESPRIT is to require calibrated antennas with uniform features, and are sensitive ti the manufacturing fault and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected Hopfield neural model. Computer simulations show the validity of the proposed algorithm. It follows that the proposed method yields better AOA estimates than MUSIC. Moreover, out method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Korean Journal of Metals and Materials
• /
• v.48 no.7
• /
• pp.589-597
• /
• 2010

In the present study, the effect of variation in alloying elements on the carbide formation behavior during casting and homogenization treatment of M2 high speed steels was investigated. M2 high speed steels of various compositions were produced by vacuum induction melting. Contents of C, Cr, W, Mo, and V were varied from the basic composition of 0.8C, 0.3Si, 0.2Mn, 4.0Cr, 6.0W, 5.0Mo, and 2.0V in weight percent. Homogenization treatment at $1150^{\circ}C$ for 1.5 hr followed by furnace cooling was performed on the ingots. Area fraction and chemical compositions of eutectic carbide in as-cast and homogenized ingots were analyzed. Area fraction of eutectic carbide appeared to be higher in the ingots with higher contents of alloying elements the area fraction of eutectic carbide also appeared to be higher on the surface regions than in the center regions of ingots. As a result of the homogenization treatment, $M_2C$ carbide, which was the primary eutectic carbide in the as-cast ingots, decomposed into thermodynamically stable carbides, MC and $M_6C$. The latter carbide was found to be the main one after homogenization. Fine carbides uniformly distributed in the matrix was found to be MC type carbide and coarsened by homogenization.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.125-134
• /
• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.77-84
• /
• 2022

In this paper, we propose a novel unsupervised feature selection method. Conventional unsupervised feature selection method defines virtual label and uses a regression analysis that projects the given data to this label. However, since virtual labels are generated from data, they can be formed similarly in the space. Thus, in the conventional method, the features can be selected in only restricted space. To solve this problem, in this paper, features are selected using orthogonal projections and low-rank approximations. To solve this problem, in this paper, a virtual label is projected to orthogonal space and the given data set is also projected to this space. Through this process, effective features can be selected. In addition, projection matrix is restricted low-rank to allow more effective features to be selected in low-dimensional space. To achieve these objectives, a cost function is designed and an efficient optimization method is proposed. Experimental results for six data sets demonstrate that the proposed method outperforms existing conventional unsupervised feature selection methods in most cases.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.661-671
• /
• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.8
• /
• pp.39-48
• /
• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.157-176
• /
• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.47-60
• /
• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.13 no.2
• /
• pp.95-109
• /
• 1993

In this study, the optimal configuration of arch structure has been tested by a decomposition technique. The object of this study is to provide the method of optimizing the shapes of both two hinged and fixed arches. The problem of optimal configuration of arch structures includes the interaction formulas, the working stress, and the buckling stress constraints on the assumption that arch ribs can be approximated by a finite number of straight members. On the first level, buckling loads are calculated from the relation of the stiffness matrix and the geometric stiffness matrix by using Rayleigh-Ritz method, and the number of the structural analyses can be decreased by approximating member forces through sensitivity analysis using the design space approach. The objective function is formulated as the total weight of the structures, and the constraints are derived by including the working stress, the buckling stress, and the side limit. On the second level, the nodal point coordinates of the arch structures are used as design variables and the objective function has been taken as the weight function. By treating the nodal point coordinates as design variable, the problem of optimization can be reduced to unconstrained optimal design problem which is easy to solve. Numerical comparisons with results which are obtained from numerical tests for several arch structures with various shapes and constraints show that convergence rate is very fast regardless of constraint types and configuration of arch structures. And the optimal configuration or the arch structures obtained in this study is almost the identical one from other results. The total weight could be decreased by 17.7%-91.7% when an optimal configuration is accomplished.

• Journal of the Korean Society of Radiology
• /
• v.13 no.5
• /
• pp.801-809
• /
• 2019

Existing Gamma Knife Radiosurgery(GKRS) for large lesions is often conducted in stages with volume or dose partitions. Often in case of volume division the target used to be divided into sub-volumes which are irradiated under the determined prescription dose in multi-sessions separated by a day or two, 3~6 months. For the entire course of treatment, treatment informations of the previous stages needs to be reflected to subsequent sessions on the newly mounted stereotactic frame through coordinate transformation between sessions. However, it is practically difficult to implement the previous dose distributions with existing Gamma Knife system except in the same stereotactic space. The treatment area is expanding because it is possible to perform the multistage treatment using the latest Gamma Knife Platform(GKP). The purpose of this study is to introduce the image-coregistration based on the stereotactic spaces and the strategy of multistage GKRS such as the determination of prescription dose at each stage using new GKP. Usually in image-coregistration either surgically-embedded fiducials or internal anatomical landmarks are used to determine the transformation relationship. Author compared the accuracy of coordinate transformation between multi-sessions using four or six anatomical landmarks as an example using internal anatomical landmarks. Transformation matrix between two stereotactic spaces was determined using PseudoInverse or Singular Value Decomposition to minimize the discrepancy between measured and calculated coordinates. To evaluate the transformation accuracy, the difference between measured and transformed coordinates, i.e., ${\Delta}r$, was calculated using 10 landmarks. Four or six points among 10 landmarks were used to determine the coordinate transformation, and the rest were used to evaluate the approaching method. Each of the values of ${\Delta}r$ in two approaching methods ranged from 0.6 mm to 2.4 mm, from 0.17 mm to 0.57 mm. In addition, a method of determining the prescription dose to give the same effect as the treatment of the total lesion once in case of lesion splitting was suggested. The strategy of multistage treatment in the same stereotactic space is to design the treatment for the whole lesion first, and the whole treatment design shots are divided into shots of each stage treatment to construct shots of each stage and determine the appropriate prescription dose at each stage. In conclusion, author confirmed the accuracy of prescribing dose determination as a multistage treatment strategy and found that using as many internal landmarks as possible than using small landmarks to determine coordinate transformation between multi-sessions yielded better results. In the future, the proposed multistage treatment strategy will be a great contributor to the frameless fractionated treatment of several Gamma Knife Centers.