• Journal of Advanced Marine Engineering and Technology
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• v.34 no.4
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• pp.522-531
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• 2010

Appropriate networks are required for the processing of various kinds of data generated on shipboard. The international standards, NMEA 2000 and IEC 61161-4, meet this requirement. NMEA 2000 is good for the processing of real time data, and IEC 61162-4 is better for the integrated management of such data. Therefore, NMEA 2000 data should be conversed into IEC 61162-4 format for the integrated management. This paper presents files and algorithm for the format conversion, and also describes their implementation.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.31-46
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• 2008

A stacking yard of a container terminal is a space for temporarily storing the containers that are carried in or imported until they are carried out or exported. If the containers are stacked in an inappropriate way, the efficiency of operation at the time of loading decreases significantly due to the rehandlings. The remarshaling is the task of rearranging containers during the idle time of transfer crane for the effective loading operations. This paper proposes a method of planning for remarshaling in a yard block of an automated container terminal. Our method conducts a search in two stages. In the first stage, the target stacking configuration is determined in such a way that the throughput of loading is maximized. In the second stage, the crane schedule is determined so that the remarshaling task can be completed as fast as possible in moving the containers from the source configuration to the target configuration. Simulation experiments have been conducted to compare the efficiency of loading operations before and after remarshaling. The results show that our remarshaling plan is really effective in increasing the efficiency of loading operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.519-526
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• 2016

As marine accidents happen frequently, it is required to establish a marine traffic monitoring system, which is designed to improve the safety and efficiency of navigation in VTS (Vessel Traffic Service). For this aim, recently, X-band marine radar is used for extracting the sea surface information and, it is necessary to retrieve wave information correctly and provide for the safe and efficient movement of vessel traffic within the VTS area. In this paper, three different current estimation algorithms including the classical least-squares (LS) fitting, a modified iterative least-square fitting routine and a normalized scalar product of variable current velocities are compared with buoy data and then, the iterative least-square method is modified to estimate wave information by improving the initial current velocity. Through several simulations with radar signals, it is shown that the proposed method is effective in retrieving the wave information compared to the conventional methods.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.389-394
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• 2023

In recent years, use of machine automation is rising in the industry. Ships also obtain machine condition information from sensor as digital information. However, on ships, crew members regularly surveil the engine room to check the condition of equipment and their information through analog gauges. This is a time-consuming and tedious process and poses safety risks to the crew while on surveillance. To address this, engine room surveillance using an autonomous mobile robot is being actively explored as a solution because it can reduce time, costs, and the safety risks for crew. Analog gauge reading using an autonomous mobile robot requires digitization for the robot to recognize the gauge value. In this study, image processing techniques were applied to achieve this. Analog gauge images were subjected to image preprocessing to remove noise and highlight their features. The center point, indicator point, minimum value and maximum value of the analog gauge were detected through image processing. Through the straight line connecting these points, the angle from the minimum value to the maximum value and the angle from the minimum value to indicator point were obtained. The obtained angle is digitized as the value currently indicated by the analog gauge through a formula. It was confirmed from the experiments that the digitization of the analog gauge using image processing was successful, indicating the equivalent current value shown by the gauge. When applied to surveillance robots, this algorithm can minimize safety risks and time and opportunity costs of crew members for engine room surveillance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.675-680
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• 2007

This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with Mel Frequency Cepstral Coefficients(MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature. vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.69-70
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• 2017

As the industry develops, the size of buildings and ships are getting bigger and more complicated. In such a complex space, emergency evacuation systems are required because of the possibility of casualties when an accident situation occurs. However, because present systems are composed of basic devices, such as alarms, emergency exit signs, and announcement regarding the situation and inform only the least information to evacuees, evacuees are not able to judge objectively. To solve these problems, various evacuation algorithms have been proposed. However, these studies aim to search evacuation routes based on specific risk factors or to model the effects of bottlenecks in evacuation situations. Therefore, there is a limit to apply to real systems. Therefore, we propose algorithms to search the optimal evacuation route considering various risk factors such as fire and bottleneck in evacuation situations and to be applicable in actual situation in this paper. Performance evaluation using computer simulations showed that the proposed scheme is effective.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.

• Journal of KSNVE
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• v.10 no.6
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• pp.1059-1066
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• 2000

In this paper. a new indirect feedback active noise control (ANC) scheme barred on the fundamental frequency estimation is proposed for systems with a harmonic noise. When reference signals necessary for feedforward ANC configuration are difficult to obtain, the conventional ANC algorithms for multi-tonal noise do not measure the reference signals but generate them with the estimated frequencies.$^{(4)}$ However, the beating phenomena, in which certain frequency components of the noise vanish intermittently, may make the adaptive frequency estimation difficult. The confusion in the estimated frequencies due to the beating phenomena makes the generated reference signals worthless. The proposed algorithm consists of two parts. The first part is a reference generator using the fundamental frequency estimation and the second one is the conventional feedforward control. We propose the fundamental frequency estimation algorithm using decision rules. which is insensitive to the beating phenomena. In addition, the proposed fundamental frequency estimation algorithm has good tracking capability and lower variance of frequency estimation error than that of the conventional cascade ANF method.$^{(4)}$ We are also able to control all interested modes of the noise, even which cannot be estimated by the conventional frequency estimation method because of the poor S/N ratio. We verify the performance of the proposed ANC method through simulations for the measured cabin noise of a passenger ship and the measured time-varying engine booming noise of a passenger vehicle.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.