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Kwang-Il Kim
Department of Marine Industry and Maritime Police, Jeju National University, Jeju 64343, Korea

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Journal article
Published: 10 June 2020 in Applied Sciences
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Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

ACS Style

Kwang-Il Kim; Keon Myung Lee. Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data. Applied Sciences 2020, 10, 4010 .

AMA Style

Kwang-Il Kim, Keon Myung Lee. Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data. Applied Sciences. 2020; 10 (11):4010.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2020. "Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data." Applied Sciences 10, no. 11: 4010.

Journal article
Published: 29 November 2019 in Sensors
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Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency.

ACS Style

Kwang-Il Kim; Keon Myung Lee. Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors 2019, 19, 5273 .

AMA Style

Kwang-Il Kim, Keon Myung Lee. Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors. 2019; 19 (23):5273.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2019. "Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning." Sensors 19, no. 23: 5273.

Journal article
Published: 19 September 2018 in Sensors
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In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion area in which there are some risks of ship collision. They want to control the traffic of the caution area at a proper level to lessen risk. Inertial ship movement makes swift changes in direction and speed difficult. It is hence important to predict future traffic of the caution area earlier on so as to get enough time for control actions on ship movements. In the harbor area, VTS stations collect a large volume of Automatic Identification Service (AIS) sensor data, which contain information about ship movement and ship attributes. This paper proposes a new deep neural network model called Ship Traffic Extraction Network (STENet) to predict the medium-term traffic and long-term traffic of the caution area. The STENet model is trained with AIS sensor data. The STENet model is organized into a hierarchical architecture in which the outputs of the movement and contextual feature extraction modules are concatenated and fed into a prediction module. The movement module extracts the features of overall ship movements with a convolutional neural network. The contextual modules consist of five separated fully-connected neural networks, each of which receives an associated attribute. The separation of feature extraction modules at the front phase helps extract the effective features by preventing unrelated attributes from crosstalking. To evaluate the performance of the proposed model, the developed model is applied to a real AIS sensor dataset, which has been collected over two years at a Korean port called Yeosu. In the experiments, four methods have been compared including two new methods: STENet and VGGNet-based models. For the real AIS sensor dataset, the proposed model has shown 50.65% relative performance improvement on average for the medium-term predictions and 57.65% improvement on average for the long-term predictions over the benchmark method, i.e., the SVR-based method.

ACS Style

Kwang-Il Kim; Keon Myung Lee. Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data. Sensors 2018, 18, 3172 .

AMA Style

Kwang-Il Kim, Keon Myung Lee. Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data. Sensors. 2018; 18 (9):3172.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2018. "Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data." Sensors 18, no. 9: 3172.

Conference paper
Published: 17 August 2018 in Advances in Intelligent Systems and Computing
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The prediction of ship destinations in the harbor can be utilized to identify future routes for navigating ships. The maritime traffic data are broadly classified into the ship trajectory data and the port information management data. These data have been accumulated for many years on the shore base station of different agencies, and are being utilized for evaluation of collision risk, prediction of vessel traffic, and other maritime statistical analysis. This paper presents a new destination prediction model of navigating ships in the harbor which consists of the candidate harbor proposal module and the position–direction filter module. The candidate harbor proposal module is trained by a deep neural network which makes use of the characteristics of ships and the occupancy distributions of piers. The position–direction filter module leaves out non-promising ones from the harbor list provided by the candidate proposal module, with respect to the current position and direction of navigating ship. In the experiments on real vessel traffic data, the proposed method has shown that its accuracy is higher than the frequency-based baseline method by about 10–15%.

ACS Style

Kwang Il Kim; Keon Myung Lee. Data-Driven Prediction of Ship Destinations in the Harbor Area Using Deep Learning. Advances in Intelligent Systems and Computing 2018, 81 -90.

AMA Style

Kwang Il Kim, Keon Myung Lee. Data-Driven Prediction of Ship Destinations in the Harbor Area Using Deep Learning. Advances in Intelligent Systems and Computing. 2018; ():81-90.

Chicago/Turabian Style

Kwang Il Kim; Keon Myung Lee. 2018. "Data-Driven Prediction of Ship Destinations in the Harbor Area Using Deep Learning." Advances in Intelligent Systems and Computing , no. : 81-90.

Journal article
Published: 16 May 2018 in Energies
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Maritime transportation is an economic form of mass transportation, but it is associated with significant energy consumption and pollutant emissions. External forces such as tidal currents, waves, and wind strongly influence the energy efficiency of ships. The effective management of external forces can save energy and reduce emissions. This study presents a method to build an optimal speed adjustment plan for a ship to navigate a given route. The method takes a dynamic programming (DP)-based approach to finding such an optimal plan to utilize external forces. To estimate the speed changes caused by external forces, the proposed method uses the mapping information from a combined database of ship status, marine environmental conditions, and speed changes. For the efficient manipulation of externally forced speed-change information, we used MapReduce-based operations that can handle big data and support the easy retrieval of associated data in specific situations. To evaluate the applicability of the proposed method, we applied it to real navigation situations in the southwestern sea of the Korean Peninsula. In the simulation experiments, we used real automatic identification system data and marine environmental data. The proposed method built more efficient speed adjustment plans than the fixed-speed navigation in terms of energy savings and pollutant emission reduction. The results also showed that the speed adjustment exploits external forces in a beneficial manner.

ACS Style

Kwang-Il Kim; Keon Myung Lee. Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction. Energies 2018, 11, 1273 .

AMA Style

Kwang-Il Kim, Keon Myung Lee. Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction. Energies. 2018; 11 (5):1273.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2018. "Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction." Energies 11, no. 5: 1273.

Journal article
Published: 30 April 2018 in Journal of Korean Institute of Intelligent Systems
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선박에 작용하는 조류 및 바람과 같은 외력은 효율적인 연료 소모운항과 항행 안전에 많은 영향을 준다. 해상에서 수집된 선박교통 및 해양환경 데이터들은 선박에 영향을 주는 외력 평가를 위해 활용되고 있다. 이 자료들은 수년 동안 우리나라 전 해역에서 수집된 방대한 자료이므로 이를 효율적으로 처리하고 신속한 분석결과를 제공하기 위한 방법이 필요하다. 이 논문에서는 선박의 기준속력으로 특정 기상환경에서 선박에 작용되는 외력을 계산하고, 처리된 데이터를 맵리듀스 빅데이터 처리 방법을 이용하여 선박 외력 빅데이터를 구축한다. 맵리듀스 처리과정의 키-값의 쌍에서, 키는 선박 항행정보를 나타내는 선박 인덱스와 해양 환경정보를 나타내는 외력 인덱스의 결합으로 구성하고, 값은 실제 선박의 속력과 기준속력의 차이로 한다. 실험을 위해 테스트 선박의 실제 속력 데이터와 선박 외력 빅데이터에 의한 예측 속력을 비교하였다. 분석결과 제안한 방법에 의한 선박속력 예측은 1노트 미만의 정확도로 실제 속력변화와 유사한 패턴을 였보다.

ACS Style

Kwang-Il Kim; Keon Myung Lee. Big Data Analysis for External Forces Acting on Ship with MapReduce Processing. Journal of Korean Institute of Intelligent Systems 2018, 28, 146 -151.

AMA Style

Kwang-Il Kim, Keon Myung Lee. Big Data Analysis for External Forces Acting on Ship with MapReduce Processing. Journal of Korean Institute of Intelligent Systems. 2018; 28 (2):146-151.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2018. "Big Data Analysis for External Forces Acting on Ship with MapReduce Processing." Journal of Korean Institute of Intelligent Systems 28, no. 2: 146-151.

Journal article
Published: 31 March 2018 in The International Journal of Fuzzy Logic and Intelligent Systems
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ACS Style

Kwang-Il Kim; Keon Myung Lee. Context-Aware Information Provisioning for Vessel Traffic Service Using Rule-Based and Deep Learning Techniques. The International Journal of Fuzzy Logic and Intelligent Systems 2018, 18, 13 -19.

AMA Style

Kwang-Il Kim, Keon Myung Lee. Context-Aware Information Provisioning for Vessel Traffic Service Using Rule-Based and Deep Learning Techniques. The International Journal of Fuzzy Logic and Intelligent Systems. 2018; 18 (1):13-19.

Chicago/Turabian Style

Kwang-Il Kim; Keon Myung Lee. 2018. "Context-Aware Information Provisioning for Vessel Traffic Service Using Rule-Based and Deep Learning Techniques." The International Journal of Fuzzy Logic and Intelligent Systems 18, no. 1: 13-19.

Conference paper
Published: 01 January 2018 in Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems
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ACS Style

Keon Myung Lee; Kyoung Soon Hwang; Kwang Il Kim; Sang Hyun Lee; Ki Sun Park. A deep learning model generation method for code reuse and automatic machine learning. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems 2018, 47 -52.

AMA Style

Keon Myung Lee, Kyoung Soon Hwang, Kwang Il Kim, Sang Hyun Lee, Ki Sun Park. A deep learning model generation method for code reuse and automatic machine learning. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. 2018; ():47-52.

Chicago/Turabian Style

Keon Myung Lee; Kyoung Soon Hwang; Kwang Il Kim; Sang Hyun Lee; Ki Sun Park. 2018. "A deep learning model generation method for code reuse and automatic machine learning." Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems , no. : 47-52.

Conference paper
Published: 01 November 2017 in 2017 European Conference on Electrical Engineering and Computer Science (EECS)
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In Vessel Traffic Service (VTS), prediction of ship traffic flow is essential for VTS operator. But, by external force such as tidal current, wind, wave and other regulations, it was difficult to predict ship traffic flow. In this paper, in order to utilize ship trajectory big data by Automatic Identification System (AIS), we propose the method to convert ship speed data to categorical data dividing ship navigating routes into several gate lines. Then experiments to verify model accuracy conduct using multiple input and output variables with artificial neural network.

ACS Style

Kwang Il Kim; Keon Myung Lee. Preprocessing Ship Trajectory Data for Applying Artificial Neural Network in Harbour Area. 2017 European Conference on Electrical Engineering and Computer Science (EECS) 2017, 147 -149.

AMA Style

Kwang Il Kim, Keon Myung Lee. Preprocessing Ship Trajectory Data for Applying Artificial Neural Network in Harbour Area. 2017 European Conference on Electrical Engineering and Computer Science (EECS). 2017; ():147-149.

Chicago/Turabian Style

Kwang Il Kim; Keon Myung Lee. 2017. "Preprocessing Ship Trajectory Data for Applying Artificial Neural Network in Harbour Area." 2017 European Conference on Electrical Engineering and Computer Science (EECS) , no. : 147-149.

Journal article
Published: 01 October 2017 in Advanced Science Letters
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Maritime safety is a major concern for all stakeholders due to the high potential risk of casualties and damage to property. Prior to entering an unfamiliar coastal area, navigators conventionally look up the ship collision accident profiles to obtain maritime safety information. Near-missship collision events are crucial in estimating the potential risk in vessel traffic. This paper presents a method for ship encounter risk evaluation, in which trajectory data of large ships is analyzed to extract ship encounter data, a probabilistic model is built to determine whether anencounter event results in a near-miss, and a risk index is proposed to evaluate the risk of a ship encounter situation resulting in a near-miss or collision. It also introduces a visualization method to effectively communicate the proposed risk index to navigators. The proposed method willbe useful to navigators for planning a safe passage.

ACS Style

Kwang-Il Kim; Keon-Myung Lee. Ship Encounter Risk Evaluation for Coastal Areas with Holistic Maritime Traffic Data Analysis. Advanced Science Letters 2017, 23, 9565 -9569.

AMA Style

Kwang-Il Kim, Keon-Myung Lee. Ship Encounter Risk Evaluation for Coastal Areas with Holistic Maritime Traffic Data Analysis. Advanced Science Letters. 2017; 23 (10):9565-9569.

Chicago/Turabian Style

Kwang-Il Kim; Keon-Myung Lee. 2017. "Ship Encounter Risk Evaluation for Coastal Areas with Holistic Maritime Traffic Data Analysis." Advanced Science Letters 23, no. 10: 9565-9569.

Conference paper
Published: 20 September 2017 in Proceedings of the International Conference on Big Data and Internet of Thing - BDIOT2017
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Machine learning algorithms have various factors to be tuned for successful application. There have been strong demands on automating the tuning process in machine learning practices. This paper characterizes the autonomicity levels at which developers feel free from burdensome tuning tasks. The autonomicity levels range from level 0 to level 4 each of which has the requirements to be supported by the machine learning framework. The paper also presents an architecture of such a machine learning framework to support automated machine learning.

ACS Style

Keon Myung Lee; Kwang Il Kim; Jaesoo Yoo. Autonomicity Levels and Requirements for Automated Machine Learning. Proceedings of the International Conference on Big Data and Internet of Thing - BDIOT2017 2017, 46 -48.

AMA Style

Keon Myung Lee, Kwang Il Kim, Jaesoo Yoo. Autonomicity Levels and Requirements for Automated Machine Learning. Proceedings of the International Conference on Big Data and Internet of Thing - BDIOT2017. 2017; ():46-48.

Chicago/Turabian Style

Keon Myung Lee; Kwang Il Kim; Jaesoo Yoo. 2017. "Autonomicity Levels and Requirements for Automated Machine Learning." Proceedings of the International Conference on Big Data and Internet of Thing - BDIOT2017 , no. : 46-48.