• 전기전자학회논문지
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• 제7권1호
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• pp.9-15
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• 2003

본 논문은 암호화 장치 및 오류정정부호화 장치 등에서 핵심적으로 사용되고 있는 유한체승산기(finite-field multiplier)의 최적화된 구조를 제안한다. 제안된 구조는 LFSR(Linear Feedback Shift Register)구조를 갖는 유한체 승산기에서 소비전력과 회로면적을 최소화 하여 기존의 LFSR 구조를 바탕으로 하는 유한체 승산기에 비하여 효율적인 승산을 이루도록 한다. 기존의 LFSR 구조의 유한체 승산기는 m비트의 다항식을 승산 하는데 3${\cdot}$m개의 플립플롭(flip-flop)이 필요하다. 1개의 플립플롭은 2개의 래치(latch)로 구성되기 때문에 6${\cdot}$m개의 래치가 소요된다. 본 논문에서는 4${\cdot}$m개의 래치(m 개의 플립플롭과 2${\cdot}$m개의 래치)로 m 비트의 다항식을 승산 할 수 있는 유한체 승산기를 제안하였다. 본 논문의 유한체 승산기는 기존의 LFSR 구조의 유한체 승산기에 비하여 회로구현에 필요한 래치의 개수가 1/3(약 33%)이 감소하였다. 결과적으로 기존의 방법에 비하여 저 소비전력 및 저 면적의 유한체 승산기를 암호화 장치 및 오류정정부호화 장치 등에서 효과적으로 사용이 가능하다.

• Smart Structures and Systems
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• 제21권5호
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.