• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.932-939
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• 2012

This paper presents an equivalent circuit model of a LIPB (Li-Ion Polymer battery) for Hybrid Electric Vehicles (HEVs). The proposed equivalent circuit can be used to predict the charging/discharging characteristics in time domain as well as the impedance characteristic analysis in frequency domain. Based on these features, a one-cell model is established as a function of Depth of Discharge (DoD), and a 48-cell model for a battery pack was also established. It was confirmed by experiment that the proposed model predict the discharging and impedance (AC) characteristics quite accurately at different constant current levels. To check the usefulness of the proposed circuit, the model was used to simulate a motor driving circuit with an Insulated Gate Bipolar Transistor (IGBT) inverter and Brushless DC (BLDC) motor, and it is confirmed that the model can calculate the battery voltage fluctuation in time domain at different DoDs.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.951-960
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• 2016

Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

• 한국인터넷방송통신학회논문지
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• 제16권3호
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• pp.103-108
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• 2016

일상생활에서 인간은 끊임없이 스트레스를 받으며 살아간다. 스트레스는 삶의 질과 밀접하게 연관이 있으며, 건강한 삶은 스트레스에 적절하게 대처하며 살아가는 삶이다. 스트레스는 호르몬 분비에 영향을 주며, 호르몬 분비의 변화는 뇌 신호 및 생체 신호에 영향을 준다. 이를 바탕으로, 본 논문은 스트레스와 뇌파 신호와의 관련성을 확인하였으며, 더 나아가 뇌파 신호 기반 정량적 스트레스 지수를 찾아보았다. 사용한 뇌파 장비는 32채널 유선 EEG 장비이며, 상업용 2채널(FP1, FP2) 뇌파 장비와의 비교를 위해, 상업용 뇌파 장비와 동일한 위치에 있는 2채널만 이용하여 데이터를 분석하였다. 뇌파의 주파수 특징점으로는 각 주파수 대역대의 파워 값, 주파수 대역대 파워 값들 간의 비율 및 차이 등을 테스트해 보았으며, 시간 특징점으로는 허스트 지수, 상관 지수, 리아프노프 지수 등을 테스트해 보았다. 총 6명의 피 실험자가 본 실험에 참여하였으며, 실험 과제로는 영어 지문이 사용되었다. 여러 특징점들 중 ${\theta}$ 파워/mid ${\beta}$ 파워가 가장 좋은 테스트 성능을 보여줬으며, 테스트 데이터에 대하여 평균 70.8%의 스트레스 분류 정확도를 얻었다. 추후, 저가 상용 2채널 뇌파 장치를 이용해서 비슷한 결과가 나오는지 확인해 볼 예정이다.

• 한국음향학회지
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• 제36권2호
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• pp.123-129
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• 2017

본 논문은 보청기의 지향성 알고리즘을 실시간으로 실현한 내용을 다루었다. 기존의 시간 영역에서의 시간 지연 기법에 의한 지향성 실현을 디지털 필터 방식으로 처리함으로써 시간 지연 적용이 불가능한 일반 DSP(Digital Signal Processing) 칩으로도 유사한 지향성 패턴을 가능하게 하였다. 시간 지연 기법과 디지털 필터 기법을 각각 Matlab(Matrix laboratory) 기반으로 비교 검증한 후에, 이를 CSR 8675 블루투스 DSP IC(Digital Signal Processing Integrated Circuit) 칩 펌웨어로 실현하고 검증해보였다. 스마트폰으로의 원격 무선 제어 기능으로 스마트 자향성 보청기의 사용자 접근 편의성을 강화시켰다.

• Composites Research
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• 제20권6호
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• pp.21-27
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• 2007

복합재 보강판에 영구히 부착된 배열 압전 능동 센서를 사용하여 저속 충격 손상을 탐지하였다. 압전 능동센서를 사용하여 구조에 램파를 전파시키기 위한 다양한 진단신호를 생성하였으며, 손상으로 인한 구조 진동의 특성 변화를 탐지하기 위하여 그 응답을 측정하였다. 이 신호 변화 특징을 한 개의 손상 지수로 표현하기 위하여 3가지 알고리즘-ADI(Active Damage Interrogation), TD RMS (Time Domain Root Mean Square), STFT(Short Time Fourier Transform) -이 검토되었다. 손상 탐지시험을 수행하여, 사용한 기법과 진단신호로 저속 충격으로 인한 두 개의 층간분리를 탐지하였으며, 그 위치를 추정하였다.

• Journal of Communications and Networks
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• 제11권6호
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1509-1518
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• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• Journal of Mechanical Science and Technology
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• 제18권9호
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• pp.1537-1548
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• 2004

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/vibration measurements. The capability is based on extracting relevant features of noise/vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.

• 대한기계학회논문집A
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• 제34권11호
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• pp.1681-1689
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• 2010

Support Vector Machine(SVM)을 이용한 회전기계 진단 연구가 많이 수행되어 왔으나 결함 분류성능은 입력 특징과 더불어 다중 분류 방법, 이진분류기, 커널함수 등에 따라 다르다. SVM 을 이용한 대부분의 기존 연구들은 한번 입력 특징들을 선정하면 결함 분류시 동일한 특징데이터를 이용한다. 본 논문에서는 회전기계의 다양한 결함조건에서 측정한 진동신호로부터 추출한 통계적 특징들을 이용하여 각각의 결함을 분류하기 위한 최적 특징들을 선정한 후, 해당 결함상태를 분류하기 위한 SVM 학습과 분류에 각각 이용하였다. 실험자료를 이용한 검증 결과, 제안한 단계 분류 방법이 상대적으로 적은 학습시간으로 단일 다중 분류 방법과 유사한 분류 성능을 얻을 수 있었다.