• Current Optics and Photonics
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• 제2권2호
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• pp.134-139
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• 2018

A novel approach for frequency-tripling vector signal generation via balanced detection without precoding and optical filter is proposed. The scheme is mainly utilizing an integrated dual-polarization quadrature phase shift keying (DPQPSK) modulator. In the DPQPSK modulator, one QPSK modulator is driven by an RF signal to generate high-order optical sidebands, while the other QPSK modulator is modulated by I/Q data streams to produce baseband vector signal as an optical carrier. After that, a frequency-tripling 16-quadrature-amplitude-modulation (16QAM) vector millimeter-wave (mm-wave) signal can be obtained by balanced detection. The proposed scheme can reduce the complexity of transmitter digital signal processing. The results show that, a 4 Gbaud baseband 16QAM vector signal can be generated at 30 GHz by frequency-tripling. After 10 km single-mode fiber (SMF) transmission, the constellation and eye diagrams of the generated vector signal perform well and a bit-error-rate (BER) below than 1e-3 can be achieved.

• 한국광학회:학술대회논문집
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• 한국광학회 2006년도 하계학술발표회 논문집
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• pp.381-382
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• 2006

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• 디지털융복합연구
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• 제20권2호
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• pp.241-250
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• 2022

COVID-19 여파로 인한 비대면 서비스와 가정 재정 불안정성의 증가로 이륜차 보험사기 발생이 예상되고 있다. 이와 함께 보험사기 수법도 갈수록 교묘해지고 있다. 하지만 비대면 배달 수요와 연관된 이륜차 교통사고와 보험사기 적발 모형 관련 연구는 매우 미흡한 실정이다. 이에 본 연구는 보험사기의 표본 편중문제를 해결하기 위해 균형 랜덤포레스트 알고리즘을 이용하고 보험사기 조사 전문가의 정성적인 판단 기준을 반영한 변수를 모델에 포함하여 적용성을 향상시키며 적발력 높은 이륜차 보험사기 모형을 개발하고자 한다. 보험사기 적발 모형 개발 결과, 기존의 비균형 랜덤 포레스트 모형에 비해 균형 랜덤 포레스트가 보험 사기혐의자를 분류하는 데 있어 통계적으로 우수한 점을 확인할 수 있었다. 특히, 총 26개의 변수를 토대로 탐색적 변수 조합을 적용한 모형의 예측 성능이 가장 높았지만 일부 변수만을 사용한 확인적 모형의 예측 성능도 크게 떨어지지 않은 와중에, 정성적인 보험사기 전문가가 선정한 변수만을 사용한 확인적 모형은 예측력이 떨어지는 것을 확인하였다. 또한, 총 26개의 변수 중 운전자 성별, 연령, 운전자 피보험자 일치 여부, 미수선 청구금액, 대인보험금 등이 중요한 변수로 확인되어 이를 활용해 이륜차 보험사기 혐의자 선별을 위한 적극적인 대처가 필요해 보인다.

• Journal of the Optical Society of Korea
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• 제20권2호
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• pp.223-227
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• 2016

The impact of optical receiver configuration on in-band crosstalk-induced penalty has been investigated in both theoretical and experimental analyses, for differential phase-shift keying (DPSK) and differential quadrature phase-shift keying (DQPSK) signals. Previously it has been shown that DPSK signals are ~6 dB more tolerant to in-band crosstalk than on-off keying (OOK) signals. However, we find that the tolerance difference between the two signals is reduced to ~3 dB when the decision threshold of the receiver is optimized to minimize the bit-error rate for each signal. Then we derive simple equations for the in-band crosstalk-induced penalty in DPSK and DQPSK signals with two different optical receiver configurations: balanced and single-ended direct-detection receivers. We confirm that the penalties obtained from our simple equations agree well with the measured results.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• 한국광학회지
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• 제11권6호
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• pp.441-446
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• 2000

Balanced Homodyne Detection 방법으로 국소 진동자의 위상을 조절해 주면서, 각 위상에 대하여 광전류를 측정하여 전류세기의 분포함수를 구하여 이 값을 라돈 역변환을 포함한 Filtered Back Projection하여 빛의 양자역학적 상태를 규정하는 위그너 함수를 구하고 이로부터 간접적으로 밀도행렬을 구한다. 또 위상에 관계없이 구해진 분포함수에서 Pattern함수를 이용하여 밀도행렬을 구할 수 있다. 본 연구에서는 위의 모든 과정을 전산시늉을 통하여 여러 양자역학적 상태 입력 광에 대하여 예측되는 위그너 함수와 밀도 행렬을 구하였다.

• 한국광학회:학술대회논문집
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• 한국광학회 1990년도 제5회 파동 및 레이저 학술발표회 5th Conference on Waves and lasers 논문집 - 한국광학회
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• pp.242-247
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• 1990

By self-homodyne method we measured the relative phase fluctuation of a light wave. Balanced detection system can eliminate local oscillator excess noise and multiport detection makes it possible ot measure the phase change of the signal beam. Deriving the SB(Signal Beam) and the LO(Local Oscillator) from the same laser source, we find the SB maintain constant phase relative to that of the LO. We have introduced a phase fluctuation in the SB by modulating the beam path of the SB. The measured phase fluctuation agreed well with the predicted one, thereby we confirmed the reliability of our system.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.29-32
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• 2003

This paper proposes an automatic pronunciation correction system which provides users with correction guidelines for each pronunciation error. For this purpose, we develop an HMM speech recognizer which automatically classifies pronunciation errors when Korean speaks foreign language. And, we collect speech database of native and nonnative speakers using phonetically balanced word lists. We perform analysis of mispronunciation types from the experiment of automatic mispronunciation detection using speech recognizer.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.432-442
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• 2019

Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.