• 한국항해항만학회지
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• 제33권10호
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• pp.709-713
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• 2009

근해의 수질오염은 수산양식 산업에 큰 영향을 미친다. 이러한 문제점을 해결하기 위하여 근해에서 외해로 양식장이 이동하고 있다. 외해 양식장의 구축을 위해서는 자동먹이 공급장치 및 원격 관리 시스템이 필요하다. 본 논문은 해상의 양식장에서의 자동 먹이 공급 시스템에 관하여 설명하고자 한다. 어류는 수온 및 어체 중량에 따라 먹이를 먹는 양이 변화하며, 해상 양식장의 경우 육상에 비하여 온도 변화가 크게 일어난다. 본 논문은 수온 및 어체 중량에 따라 먹이량을 계산하고 자동으로 먹이를 공급하는 시스템을 연구하였으며 모형 실험을 통하여 먹이 공급 장치의 성능을 검증하였다.

• Journal of Positioning, Navigation, and Timing
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• 제3권3호
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• pp.107-116
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• 2014

When an incoming Global Positioning System (GPS) signal is acquired, pull-in search performs a finer search of the Doppler frequency of the incoming signal so that phase lock loop can be quickly stabilized and the receiver can produce an accurate pseudo-range measurement. However, increasing the accuracy of the Doppler frequency estimation often involves a higher computational cost for weaker GPS signals, which delays the position fix. In this paper, we show that the Doppler frequency detectable by a long coherent auto-correlation can be accurately estimated using a complex-weighted sum of consecutive short coherent auto-correlation outputs with a different Doppler frequency hypothesis, and by exploiting this we propose a noise resistant, low-cost and highly accurate Doppler frequency and phase estimation technique based on a reverse directional application of the finite rate of innovation (FRI) technique. We provide a performance and computational complexity analysis to show the feasibility of the proposed technique and compare the performance to conventional techniques using numerous Monte Carlo simulations.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2013년도 춘계학술대회
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• pp.17-20
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• 2013

본 연구는 외란하에서 선박 트랙 키핑에 대한 수치시뮬레이션 문제를 다루었다. 이용된 선박 모델은 새누리호 선박을 이용하여, 목포항 입구부터, 목표 부두까지 항로에서의 항로 추종를 자동제어 기법을 이용하여 시뮬레이션 수행하였다. 기존 과거 연구의 선박 트랙 키핑 문제는 주로 정속에서 수행되었으나, 본 연구에서 부두에 접안하기 전 단계까지를 감안하여, 선박 속도를 저하시키며, 접안 하기 직전 선박이 목표 지점에 도달하여 정지할 때까지의 트랙 키핑 문제를 다루었다. 사용 제어 기법은 PID기법, 외란으로 바람 영향을 고려한 트랙키핑 시뮬레이션을 수행하였고, 그 결과 접안 직전 지점까지 적정하게 속도를 저하시키며, 원하는 항로를 따라 자동 제어 됨을 알 수 있었다.

• 한국항해항만학회지
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• 제28권9호
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• pp.803-808
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• 2004

In this paper, an INS compensation algorithm is proposed using the accelerometer from IMU. First, we denote the basic INS algorithm and show that how to compensate the position error when low cost IMU is used. Second, considering the ship's characteristic and ocean environments, we consider with a drift as a periodic external environment change which is affected with exact position. To develop the compensation algorithm, we use a repetitive method to reduce the external environment changes. Lastly, we verify the proposed algorithm through the experiments, where the acceleration sensor is used to acquire real data.

• Journal of Positioning, Navigation, and Timing
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• 제8권1호
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• pp.13-18
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• 2019

This paper analyzes the signal acquisition performance of the legacy GNSS spreading codes and a polyphase code. The code length and chip rate of a polyphase code are assumed to be same as those of the GPS L1 C/A and Galileo E1C codes. The autocorrelation and cross correlation characteristics are analyzed. In addition, a way to calculate a more accurate probability of false alarm for a code with sidelobe non-zero auto-correlation function is proposed. Finally, we estimate the probability of detection and the mean acquisition time for a given signal strength and the probability of false alarm.

• 해양환경안전학회지
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• 제13권2호
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• pp.161-167
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• 2007

이 논문은 선박의 자동조타장치를 음성인식으로 제어할 수 있는 시스템을 개발하기 위한 기초연구로 SMCP(IMO Standard Marine Communication Phrases)에 제시된 조타명령문의 구성 형태를 분석하여 화자의 의도를 예측할 수 있는 특정 파라미터를 추출하였다. 그리고 이 파라미터를 이용하여 1차 패턴인식 과정으로부터 도출된 후보단어 집합으로부터 최종 단어를 결정하는 후처리 인식 프로시저를 설계하였다. 이 프로시저의 유용성을 검증하기 위하여 음성인식용으로 총 525개의 조타명령문을 획득하였고, 표준패턴 기반의 인식과정 인식률과의 비교실험을 수행하였다. 실험결과 의도예측 특정 파라미터를 이용한 인식 프로시저의 인식률이 약 42.3% 향상되어 유효함을 알 수 있었다.

• 로봇학회논문지
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• 제2권1호
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• pp.40-47
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• 2007

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

• Journal of Biosystems Engineering
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• 제33권1호
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• pp.58-62
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• 2008

The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

• Journal of Positioning, Navigation, and Timing
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• 제2권1호
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• pp.1-8
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• 2013

A signal system for improving the code acquisition complexity of Global Navigation Satellite System (GNSS) receiver is proposed and the receiving correlator scheme is presented accordingly. The proposed signal system is a hierarchical code type with a duplexing configuration which consists of the Zadoff-Chu (ZC) code having a good auto-correlation characteristic and the Pseudo Random Noise (PRN) code for distinguishing satellites. The receiving correlator has the scheme that consists of the primary correlator for the ZC code and the secondary correlator which uses the PRN code for the primary correlation results. The simulation results of code acquisition using the receiving correlator of the proposed signal system show that the proposed signal scheme improves the complexity of GNSS receiver and has the code acquisition performance comparable to the existing GNSS signal system using Coarse/Acquisition (C/A) code.

• Journal of Positioning, Navigation, and Timing
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• 제12권1호
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.