• 한국정보통신학회논문지
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• 제10권4호
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• pp.752-757
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• 2006

고속데이터 전송이 요구되는 경우, OFDM(Orthogonal Frequency Division Multiplexing)은 다중경로에 의해 발생되는 주파수 선택성 페이딩에 쉽게 대처할 수 있다는 장점 때문에 다양한 고속 무선 통신 시스템에 채택되어왔다. 본 논문에서는 최적의 적응 비트로딩 알고리즘을 제안하고, 이를 확인하기 위해 SISO(Single Input Single Output)-OFDM 시스템에 이 알고리즘을 적용하고 고정 변조를 사용하는 SISO-OFDM과 비교 분석 하였다. 특히 다중 경로페이딩 채널에서 채널을 알고 있는 경우, MIMO(Multiple Input Multiple Output) 시스템의 적응 비트로딩을 시험하기 위해, 특이치 분해(SVD : Singular Value Decomposition)를 사용하여 MIMO 채널을 SISO 채널로 병렬 분해하여, 제안한 적응비트로딩 알고리즘을 적용하였다. 시뮬레이션 결과, 적응 비트로딩 MIMO-OFDM 시스템은 SISO-OFDM 시스템 보다 BER 성능이 우수함을 확인하였다.

• Journal of Communications and Networks
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• 제17권5호
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• 한국광학회지
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• 제4권4호
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• pp.363-372
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• 1993

설계변수의 직교화와 SVD(singular value decomposition)를 최적화에 도입하고, 이를 double-Gauss형 사진렌즈계에 적용시켜 최적화의 수렴성과 안정성을 일반적인 최소자승법, 감쇠최소자승법의 경우와 비교하였다. 최적화에서 정규방정식의 조건수(고유값의 최대, 최소값의 비)가 최적화의 불안정성과 밀접한 관련이 있다는 것은 이미 알려져 있다. 본 연구에는 SVD를 도입하여 조건수를 제한하여 본 결과 최적화의 안정성이 매우 증진 되었으며, 감쇠최소자승법에서 적은 감쇠항을 주고 직교화와 SVD를 적용시킨 경우가 가장 빠르고 안정하게 수렴하였다. 이것은 변수의 직교화와 SVD가 감쇠최소자승법에서 적은 감쇠항을 줄 때 생기는 불안정성을 잘 극복하고 있음을 나타내고 있다.

• Journal of Power Electronics
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• 제16권3호
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• pp.936-945
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• 2016

Large magnitude common-mode voltage (CMV) and its variation dv/dt have an adverse effect on motor drives that leads to early winding failure and bearing deterioration. For matrix converters, the switch states that connect each output line to a different input phase result in the lowest CMV among all of the valid switch states. To reduce the output CMV for matrix converters, this paper presents a new space vector modulation (SVM) strategy by utilizing these switch states. By this mean, the peak value and the root mean square of the CMV are dramatically decreased. In comparison with the conventional SVM methods this strategy has a similar computation overhead. Experiment results are shown to validate the effectiveness of the proposed modulation method.

• Structural Engineering and Mechanics
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• 제14권2호
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• pp.151-170
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• 2002

Moving force identification is a very important inverse problem in structural dynamics. Most of the identification methods are eventually converted to a linear algebraic equation set. Different ways to solve the equation set may lead to solutions with completely different levels of accuracy. Based on the measured bending moment responses of the bridge made in laboratory, this paper presented the time domain method (TDM) and frequency-time domain method (FTDM) for identifying the two moving wheel loads of a vehicle moving across a bridge. Directly calculating pseudo-inverse (PI) matrix and using the singular value decomposition (SVD) technique are adopted as means for solving the over-determined system equation in the TDM and FTDM. The effects of bridge and vehicle parameters on the TDM and FTDM are also investigated. Assessment results show that the SVD technique can effectively improve identification accuracy when using the TDM and FTDM, particularly in the case of the FTDM. This improved accuracy makes the TDM and FTDM more feasible and acceptable as methods for moving force identification.

• 한국광학회지
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• 제6권3호
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• pp.178-187
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• 1995

렌즈 설계에 적합한 감쇠계수를 결정하는 방법을 연구하였다. 구해진 감쇠계수를 additive 감쇠최소 자승법에 도입하였을 때, triplet형 사진렌즈 설계에서 최적화 과정의 수렴성과 안정성에 관해서 조사하였다. 오차함수의 Jacobian 행열의 곱에 대한 고유값을 SVD(singular value decomposition)를 통해 구한 후 고유값들의 중간치를 적합한 감쇠계수로 결정하였다. 적합한 감쇠계수를 이용하여 triplet형 사진렌즈를 최적화한 결과 수렴성과 안정성이 향상되었다. 직교변환 방법으로 Jacobian 행열의 해를 구하면 정규 방정식을 사용하여 해를 구할 때 발생하는 수치적 부정확성을 개선할 수 있었다. 적합한 감쇠계수 선택법과 Jacobian 행열의 직교변환 방법을 같이 사용하는 것이 고차의 항들을 가지는 비구면 렌즈 설계에 유용함을 알 수 있었다.

• 전기학회논문지
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• 제57권4호
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1708-1727
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• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• 한국산업정보학회논문지
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• 제15권2호
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• pp.49-57
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• 2010

본 논문에서는 역전파 신경망 알고리즘(BPNN: Back Propagation Neural Network)과 Singular Value Decomposition(SVD)를 이용하는 한글 문서 분류 시스템을 제안한다. BPNN은 학습을 통하여 만들어진 네트워크를 이용하여 문서분류를 수행한다. 이 방법의 어려움은 분류기에 입력되는 특징 공간이 너무 크다는 것이다. SVD를 이용하면 고차원의 벡터를 저차원으로 줄일 수 있고, 또한 의미있는 벡터 공간을 만들어 단어 사이의 중요한 관계성을 구축할 수 있다. 본 논문에서 제안한 BPNN의 성능 평가를 위하여 한국일보-2000/한국일보-40075 문서범주화 실험문서집합의 데이터 셋을 이용하였다. 실험결과를 통하여 BPNN과 SVD를 사용한 시스템이 한글 문서 분류에 탁월한 성능을 가지는 것을 보여준다.

• 인터넷정보학회논문지
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• 제25권1호
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.