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Power is essential in nonferrous arc furnace plants for burning and melting scraps, as well as for the composition of raw materials to produce the furnace product. To ensure high-quality operation, the electric energy is controlled by changing the tap positions. However, there is no standard working pattern to determine the most effective option for changing the tap positions to obtain optimal power and product quantity. This study proposes a method to analyze and determine the working patterns in nonferrous arc furnace plants by adopting dynamic programming. To find the best objective value candidates, statistical methods were utilized to obtain the optimal values of the total elemental power and total product quantity. Moreover, if the maximum product quantity minimum electric consumption are known, the least power per product quantity (PPQ) can be easily obtained. Thus, it is reasonable to analyze the sequences of tap positions and then obtain the best PPQ using an approach of solving a recurrence problem with the widely used dynamic programming approach. We demonstrated that the proposed method suggested the working pattern of tap positions, thereby providing relatively good PPQs in comparison with the conventional method.
Sokchomrern Ean; Manas Bazarbaev; Keon Myung Lee; Aziz Nasridinov; Kwan-Hee Yoo. Dynamic programming-based computation of an optimal tap working pattern in nonferrous arc furnace. The Journal of Supercomputing 2021, 1 -27.
AMA StyleSokchomrern Ean, Manas Bazarbaev, Keon Myung Lee, Aziz Nasridinov, Kwan-Hee Yoo. Dynamic programming-based computation of an optimal tap working pattern in nonferrous arc furnace. The Journal of Supercomputing. 2021; ():1-27.
Chicago/Turabian StyleSokchomrern Ean; Manas Bazarbaev; Keon Myung Lee; Aziz Nasridinov; Kwan-Hee Yoo. 2021. "Dynamic programming-based computation of an optimal tap working pattern in nonferrous arc furnace." The Journal of Supercomputing , no. : 1-27.
Data are collected and regarded as valuable assets in many business domains. Their owner would not want to disclose them to the public due to their potential value. Distributed knowledge discovery techniques have been proposed which assume the cooperation of data owners even though they might not behave in a trustworthy manner. When a party decides to quit the cooperation in the distributed knowledge discovery, the other parties cannot continue the discovery task and hence they get some disadvantage due to the party’s betrayal. This paper is concerned with data privacy-preserving distributed knowledge discovery which gives penalty to the party who quits the cooperation in the discovery process. It proposes a blockchain-based distributed machine learning method which does not disclose the participating parties’ data and gives the penalty to betraying parties. The proposed method makes the participating parties communicate with each other via the smart contract on the blockchain network. It uses a blockchain-based incentive system to establish trust among parties and to improve the quality of discovery knowledge. The proposed method has been implemented with a smart contract on the blockchain and tested for a benchmark data.
Keon Myung Lee; Ilkyeun Ra. Data privacy-preserving distributed knowledge discovery based on the blockchain. Information Technology and Management 2020, 21, 191 -204.
AMA StyleKeon Myung Lee, Ilkyeun Ra. Data privacy-preserving distributed knowledge discovery based on the blockchain. Information Technology and Management. 2020; 21 (4):191-204.
Chicago/Turabian StyleKeon Myung Lee; Ilkyeun Ra. 2020. "Data privacy-preserving distributed knowledge discovery based on the blockchain." Information Technology and Management 21, no. 4: 191-204.
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.
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 StyleKwang-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 StyleKwang-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.
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.
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 StyleKwang-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 StyleKwang-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.
Keon Myung Lee; Ki‐Sun Park; Kyung‐Soon Hwang; Kwang‐Il Kim. Deep neural network model construction with interactive code reuse and automatic code transformation. Concurrency and Computation: Practice and Experience 2019, 32, 1 .
AMA StyleKeon Myung Lee, Ki‐Sun Park, Kyung‐Soon Hwang, Kwang‐Il Kim. Deep neural network model construction with interactive code reuse and automatic code transformation. Concurrency and Computation: Practice and Experience. 2019; 32 (18):1.
Chicago/Turabian StyleKeon Myung Lee; Ki‐Sun Park; Kyung‐Soon Hwang; Kwang‐Il Kim. 2019. "Deep neural network model construction with interactive code reuse and automatic code transformation." Concurrency and Computation: Practice and Experience 32, no. 18: 1.
Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning. In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information.
Keon Myung Lee; Jaesoo Yoo; Sang-Wook Kim; Jee-Hyong Lee; Jiman Hong. Autonomic machine learning platform. International Journal of Information Management 2019, 49, 491 -501.
AMA StyleKeon Myung Lee, Jaesoo Yoo, Sang-Wook Kim, Jee-Hyong Lee, Jiman Hong. Autonomic machine learning platform. International Journal of Information Management. 2019; 49 ():491-501.
Chicago/Turabian StyleKeon Myung Lee; Jaesoo Yoo; Sang-Wook Kim; Jee-Hyong Lee; Jiman Hong. 2019. "Autonomic machine learning platform." International Journal of Information Management 49, no. : 491-501.
Blockchain enables users to be autonomous without the need of trust by using a digital ledger of decentralized consensus. Blockchain-based IoT allows service providers to transfer the security and maintenance responsibility to self-maintaining customers. Adopting peer-to-peer (P2P) networking and computing for more than billions of transactions can reduce the costs arising from the installation and maintenance of centralized systems. Small manufacturers providing industrial IoT (IIoT) services can participate more actively in blockchain-based IIoT applications with three-dimensional printing and digital manufacturing technologies, but the measure to maintain privacy on a blockchain is not robust. Here, we propose a P2P networking-based custom manufacturing service, which is an order-driven trading service between a manufacturer and a customer in the blockchain-based IIoT architecture. The proposed system consists of reputation management and service architecture. We propose a new reputation assessment method customized to increase the reliability and accuracy of industrial manufacturing systems. We also propose a manufacturer rating classification to guide the customers’ decision making in a reliable manner and a malicious evaluator identification to exclude feedbacks from malicious evaluators. The proposed service architecture is composed of trustless P2P protocols designed for preserving privacy and providing non-repudiation. We used a cryptographic algorithm for ensuring transaction privacy and the digital signing of blockchain for non-repudiation. We also analyzed the proposed service architecture and the possible attack scenarios to verify the security requirements. We verified that the reputation management system was influenced by each feedback dynamically and guided the customer’s present decision making with reliable and classified manners, by simulating reputation classification and malicious evaluator identification. Further, we have summarized the originality and the characteristics of the proposed approach by comparing closely related studies and concluded with a future research guide.
YongJoo Lee; Keon Myung Lee; Sang Ho Lee. Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment. Peer-to-Peer Networking and Applications 2019, 13, 671 -683.
AMA StyleYongJoo Lee, Keon Myung Lee, Sang Ho Lee. Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment. Peer-to-Peer Networking and Applications. 2019; 13 (2):671-683.
Chicago/Turabian StyleYongJoo Lee; Keon Myung Lee; Sang Ho Lee. 2019. "Blockchain-based reputation management for custom manufacturing service in the peer-to-peer networking environment." Peer-to-Peer Networking and Applications 13, no. 2: 671-683.
Communication systems consist of many subsystems and components among which various stream data including control messages as well as payload messages are transferred. Some messages can be regarded as events which are identifiable occurrence that has significance for system. Those events can be categorized into instant events and persistent ones according to whether they has duration in which some state is kept continuously. Instant events are treated as having no duration, while persistent events have some duration. Most conventional event sequence mining techniques do not consider the persistent events in which they treat persistent events as instant ones. Once persistent events come into play, event sequence patterns need to take into account occurrence constraints which indicate which persistent events are active when some instant or persistent event occurrence is observed. This paper proposes an event sequence pattern mining method which identifies frequent event sequences in which each event may have its associated persistent events as its co-occurrence constraints. The proposed method uses a sliding window technique which advances one event occurence at a time to get exact support count in the mixed stream of instant events and persistent events. It is equipped with an efficient pattern generation technique using dynamic programming technique, and an effecient counting technique for counting the occurrences of specific patterns. It has been implemented and evaluated for the experimental studies for data sets.
Keon Myung Lee; Chan Sik Han; Joong Nam Jun; Jee Hyong Lee; Sang Ho Lee. Batch-Free Event Sequence Pattern Mining for Communication Stream Data with Instant and Persistent Events. Wireless Personal Communications 2018, 105, 673 -689.
AMA StyleKeon Myung Lee, Chan Sik Han, Joong Nam Jun, Jee Hyong Lee, Sang Ho Lee. Batch-Free Event Sequence Pattern Mining for Communication Stream Data with Instant and Persistent Events. Wireless Personal Communications. 2018; 105 (2):673-689.
Chicago/Turabian StyleKeon Myung Lee; Chan Sik Han; Joong Nam Jun; Jee Hyong Lee; Sang Ho Lee. 2018. "Batch-Free Event Sequence Pattern Mining for Communication Stream Data with Instant and Persistent Events." Wireless Personal Communications 105, no. 2: 673-689.
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.
Kwang-Il Kim; Keon Myung Lee. Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data. Sensors 2018, 18, 3172 .
AMA StyleKwang-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 StyleKwang-Il Kim; Keon Myung Lee. 2018. "Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data." Sensors 18, no. 9: 3172.
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%.
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 StyleKwang 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 StyleKwang 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.
The deployment of various high-throughput instruments in molecular biology has produced a large of volume of heterogeneous experimental omics data. It is of paramount importance to collect and organize different types of those data in a unified manner so as to disseminate those valuable data to the researchers of interest. One of the essential components in such data organization and dissemination is to maintain appropriate metadata which help understand collected data. The metadata should be defined sufficient enough to provide necessary information for the researchers who want to use them. This paper presents a metadata modeling method tailored to experimental omics data which are generated for specific biological specimens. The method first identifies the candidate keywords by analyzing the literature and then they are compared with items used in other biological metadata. The candidate keywords are examined and organized into categories so that the organized keywords provide a unified view of metadata for heterogeneous collection of experimental omics data. The metadata modeling results for experimental omics data of human tissues are expressed in database schema. The proposed method has been successfully applied to metadata modeling for experimental omics data in the Biobank of Korea Centers of Disease Control and Prevention.
Kyoung Soon Hwang; Keon Myung Lee. Keyword-Based Metadata Modeling for Experimental Omics Data Dissemination. Advances in Intelligent Systems and Computing 2018, 139 -150.
AMA StyleKyoung Soon Hwang, Keon Myung Lee. Keyword-Based Metadata Modeling for Experimental Omics Data Dissemination. Advances in Intelligent Systems and Computing. 2018; ():139-150.
Chicago/Turabian StyleKyoung Soon Hwang; Keon Myung Lee. 2018. "Keyword-Based Metadata Modeling for Experimental Omics Data Dissemination." Advances in Intelligent Systems and Computing , no. : 139-150.
Various machine learning algorithms are available off the shelf, even for free. It takes an expert to choose a proper algorithm for given task and to set hyperparameters of the algorithm. This paper addresses an architecture of autonomic machine learning platform with which developers get some assistance in choosing a machine learning algorithm appropriate to a task and in selecting the values of hyperparameters of the algorithm. Due to massive computation demands on executing machine learning algorithms, the platform is designed to utilize the external computing resources such as cloud computing systems and distributed computing systems. This paper presents the design choices and architecture of the proposed platform, and a possible application to intelligent databases.
Keon Myung Lee; Jaesoo Yoo; Jiman Hong. Autonomic Machine Learning for Intelligent Databases. Lecture Notes in Electrical Engineering 2018, 163 -169.
AMA StyleKeon Myung Lee, Jaesoo Yoo, Jiman Hong. Autonomic Machine Learning for Intelligent Databases. Lecture Notes in Electrical Engineering. 2018; ():163-169.
Chicago/Turabian StyleKeon Myung Lee; Jaesoo Yoo; Jiman Hong. 2018. "Autonomic Machine Learning for Intelligent Databases." Lecture Notes in Electrical Engineering , no. : 163-169.
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.
Kwang-Il Kim; Keon Myung Lee. Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction. Energies 2018, 11, 1273 .
AMA StyleKwang-Il Kim, Keon Myung Lee. Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction. Energies. 2018; 11 (5):1273.
Chicago/Turabian StyleKwang-Il Kim; Keon Myung Lee. 2018. "Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction." Energies 11, no. 5: 1273.
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.
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 StyleKwang-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 StyleKwang-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.
Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.
Kwang Ii Kim; Keon Myung Lee; Jang Young Ahn. Methods of ship trajectory data processing for applying artificial neural network in port area. International Journal of Engineering & Technology 2018, 7, 145 -146.
AMA StyleKwang Ii Kim, Keon Myung Lee, Jang Young Ahn. Methods of ship trajectory data processing for applying artificial neural network in port area. International Journal of Engineering & Technology. 2018; 7 (2.12):145-146.
Chicago/Turabian StyleKwang Ii Kim; Keon Myung Lee; Jang Young Ahn. 2018. "Methods of ship trajectory data processing for applying artificial neural network in port area." International Journal of Engineering & Technology 7, no. 2.12: 145-146.
Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.
Kwang Il Kim; Keon Myung Lee. Mining of missing ship trajectory pattern in automatic identification system. International Journal of Engineering & Technology 2018, 7, 167 -170.
AMA StyleKwang Il Kim, Keon Myung Lee. Mining of missing ship trajectory pattern in automatic identification system. International Journal of Engineering & Technology. 2018; 7 (2.12):167-170.
Chicago/Turabian StyleKwang Il Kim; Keon Myung Lee. 2018. "Mining of missing ship trajectory pattern in automatic identification system." International Journal of Engineering & Technology 7, no. 2.12: 167-170.
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 StyleKwang-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 StyleKwang-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.
Sachin Bhardwaj; Tanir Ozcelebi; Johan J. Lukkien; Keon Myung Lee. Semantic Interoperability Architecture for Smart Spaces. INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 2018, 18, 50 -57.
AMA StyleSachin Bhardwaj, Tanir Ozcelebi, Johan J. Lukkien, Keon Myung Lee. Semantic Interoperability Architecture for Smart Spaces. INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS. 2018; 18 (1):50-57.
Chicago/Turabian StyleSachin Bhardwaj; Tanir Ozcelebi; Johan J. Lukkien; Keon Myung Lee. 2018. "Semantic Interoperability Architecture for Smart Spaces." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 18, no. 1: 50-57.
Writing essays and technical documents can be a challenging task for many people, especially for non-native speakers. Good content and ideas are both important in writing, but clear and effective expressions that can accurately convey the meaning of these ideas to the readers are essential for good writing. Many writers often face difficulty in selecting the proper words that would fit into their sentences. Proper words may be widely used words that appear in similar contexts. These can be identified by a statistical analysis of a corpus, which is a collection of a large number of sentences. This paper propses a method that can recommend suitable words based on word pattern queries, which are expressed as a combination of words, part-of-speech (POS) tags, and wild card words, such as ‘ {1:2} idea.’ The proposed method enables to recommend some words for the POS tags of a word pattern query, along with their popularity and example sentences in a corpus. To facilitate such query processing, the method first conducts the POS tagging for all the sentences in a corpus. From the tagged sentences, it generates the 2-grams up to 5-grams, which consist of words, POS tags, and the special wild card word symbol ‘*’. It then builds an inverted file-like data structure which keeps the relevant information for each potential word pattern from the n-grams. Due to the large number of word patterns and sentences, the MapReduce algorithms are developed to realize the proposed method and HBase are deployed to manage the inverted file-like data structure. Some experiment results are presented to show the characteristics of the proposed method.
Keon Myung Lee; Chan-Sik Han; Kwang-Il Kim; Sang Ho Lee. Word recommendation for English composition using big corpus data processing. Cluster Computing 2018, 22, 1911 -1924.
AMA StyleKeon Myung Lee, Chan-Sik Han, Kwang-Il Kim, Sang Ho Lee. Word recommendation for English composition using big corpus data processing. Cluster Computing. 2018; 22 (S1):1911-1924.
Chicago/Turabian StyleKeon Myung Lee; Chan-Sik Han; Kwang-Il Kim; Sang Ho Lee. 2018. "Word recommendation for English composition using big corpus data processing." Cluster Computing 22, no. S1: 1911-1924.
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 StyleKeon 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 StyleKeon 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.