• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.165-173
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• 2021

Apophis is a near-Earth object with a diameter of approximately 340 m, which will come closer to the Earth than a geostationary orbit in 2029, offering a unique opportunity for characterizing the object during the upcoming encounter. Therefore, Korea Astronomy and Space Science Institute has a plan to propose a space mission to explore the Apophis asteroid using scientific instruments such as a laser altimeter. In this study, we evaluate the performance metrics of a laser altimeter using a pseudorandom noise modulation technique for the Apophis mission, in terms of detection probability and ranging accuracy. The closed-form expression of detection probability is provided using the cross correlation between the received pulse trains and pseudo-random binary sequence. And the new ranging accuracy model using Gaussian error propagation is also derived by considering the sampling rate. The operation range is significantly limited by thermal noise rather than background noise, owing to not only the low power laser but also the avalanche photodiode in the analog mode operation. However, it is demonstrated from the numerical simulation that the laser altimeter can achieve the ranging performance required for a proximity operation mode, which employs commercially available components onboard CubeSat-scale satellites for optical communications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.535-543
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• 2002

In this paper, we propose $\pi$/8 shift 8PSK and trellis-coded $\pi$/8 shift 8PSK-OFDM techniques by applying $\pi$/4 shift QPSK to trellis-coded modulation (TCM), and performing signal set expansion and set partition correspondingly based on phase difference. In our Viterbi decoding algorithm, up to L phase differences from successively received symbols are employed in the new branch metrics. Such sliding multiple symbol detection (SMSD) method provides improved bit-error-rate (BER) performance in the differential detection of the trellis-coded $\pi$/8 shift 8PSK-OFDM signals. The performance improvements are achieved for different communication channels without sacrificing bandwidth and power efficiency. It thus makes the proposed modulation and sliding detection scheme more attractive for power and band-limited systems.

• Smart Structures and Systems
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• v.29 no.1
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• pp.167-179
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• 2022

This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.484-495
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• 2014

This paper introduces the use of network theory to model and analyze disparate ship design information. This work will focus on a ship's distributed systems and their intra- and intersystem structures and interactions. The three system to be analyzed are: a passageway system, an electrical system, and a fire fighting system. These systems will be analyzed individually using common network metrics to glean information regarding their structures and attributes. The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions. Network theory will be shown to be useful in the early design stage due to its simplicity and ability to model any shipboard system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.662-663
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• 2014

In most vehicle manufactures have traditionally relied on find-fix method of human auditor, mainly due to variation excitation source. To solve the BSR noise, the requirements for BSR test are presented in terms of detection of noise source, analysis of time-frequency and sound pressure, sound quality for noise. A number of new technology direction, particularly in the field of noise source identification application and psycho-acoustics from the Zwicker's sound quality parameter, the computed objective sound metrics and subjective jury test result.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.59-60
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• 2007

Entropy, one of leading metrics on anomalous traffic, attracts researcher's attention since a packet sampling and a traffic volume impact little on entropy value. In this paper, we apply the entropy metric to a domestic network traffic trace which has real anomalous traffics. We used source IP address/port and destination IP address/port that are important attributes of a packet as entropy variable We found that entropy value of multiple-port DoS attack shows something related to a staircase fashion. Also, we show a Possibility of detection of anomalous traffic on small time scale.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.709-712
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• 2006

표절을 검사하는 방법으로는 문서 내의 특정 정보들을 추출하여 비교하는 지문법(fingerprint)과 파스트리(parse tree)와 같이 프로그램의 특정한 구조를 이용하여 문서의 구조적 유사성을 검사하는 구조적(structure metrics) 검사방법들이 있다. 본 논문에서는 표절검사를 위한 프로그램 추적 기법을 제안한다. 프로그램 추적 기법은 프로그램을 구문단계에서 정적으로 수행을 하여 그 수행되는 함수들의 순서에 따라 주요 키워드를 추출하여 새롭게 정렬하는 방법이다. 실험결과 사용하지 않는 코드 삽입, 함수 위치 변경 및 합성 등과 같은 표절 스펙트럼에서 정의한 표절 방법에 대하여 효과적으로 검출할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.651-654
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• 2004

In this paper, high-level events that adjust or control the conflicts between blocks or process statement, or job sequences are defined compared to low-level events. This paper proposes that high-level events consist of resources conflicts and protocol or specification-dependent conflicts, and two low-level coverage metrics can be used to defect high-level events.