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Early Anomaly Detection of LNG-Carrier Main Engine System based on Multivariate Time-Series Boundary Forecasting and Confidence Evaluation Technique
Donghyun Kim, Taigon Kim, Minji An, Yunju Baek
http://doi.org/10.5626/JOK.2023.50.5.429
Recently, a variety of studies have been conducted to detect abnormal operation of ships and their causes and in the marine and shipbuilding industries. This study proposed a method for early anomaly detection of the main engine system using a multivariate time series sensor data extracted from LNG carriers built at a shipyard. For early anomaly detection, the process of predicting the future value through the sensor data at present is necessary, and in this process, the prediction residual, which is the difference between the actual future value and the predicted value, is generated. Since the generated residual has a significant effect on the early anomaly detection results, a compensating process is necessary. We propose novel loss functions that can learn the upper or lower prediction boundary of a time-series forecasting model. The time-series forecasting model trained with the proposed loss function improves the performance of the early anomaly detection algorithm by compensating the prediction residual. In addition, the real-time confidence of the predicted value is evaluated through the newly proposed confidence model by utilizing the similarity between time-series forecasting residual and confidence residual. With the early anomaly detection algorithm proposed in this study, the prediction model, which learns the upper boundary, outputs the upper limit of the predicted value that can be output by the baseline prediction model learned with the MSE loss function and can predict abnormal behavior that threshold-based anomaly discriminator could not predict because the future prediction of the baseline model is lower than the actual future value. Based on the results of this study, the performance of the proposed method was improved to 0.9532 compared to 0.4001 of the baseline model in Recall. This means that robust early anomaly detection is possible in various operating styles of the actual ship operations.
A Pre-processing Method for Learning Data Using eXplainable Artificial Intelligence
Changhong Lee, Jaemin Lee, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2023.50.2.133
Artificial intelligence model generation proceeds to the stages of learning data processing, model learning, and model evaluation. Data pre-processing techniques for creating quality learning data contribute many of the methods for improving model accuracy. Existing pre-processing techniques tend to rely heavily on the experience of model generators. If pre-processing is performed based on experience, it is difficult to explain the basis for selecting the corresponding pre-processing technique. However, the reason why generators are forced to rely on experience is that the learning model becomes huge and complicated to a level that is difficult for humans to interpret. Therefore, research is being conducted to explain the operation method of the model by introducing eXplainable AI. In this paper, we propose a learning data pre-processing system using eXplainable AI. The system operation process is trained with data that has not been pre-processed, the learned model is analyzed using eXplainable AI, and the data pre-processing is repeated based on that information. Finally, we will improve the model performance, explain pre-processing reliability, and show the practicality of the system.
AoI based Data Freshness Improvement Possibility Analysis for Industrial Automation Systems
Gukcheol Choi, Junhwan Huh, Yunseob Kim, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2022.49.9.788
Recently, industrial automation systems have been used with various equipment and industrial protocols to provide high-level process automation. When performing real-time status information update in a network composed of various protocols, transmission delay can occur if the data processing speed of each protocol is not considered when transmitting and receiving data between heterogeneous protocols. Transmission delay causes problems with monitoring and controlling based on the old state information. Therefore, it is important to update the status information at appropriate intervals so that the destination system receives fresh information. In this paper, we analyze the problem caused by the difference in the data processing speed of each protocol during heterogeneous EtherCAT and OPC UA protocol communication using AoI (Age of Information), a metric of data freshness, and then we propose a method to find an appropriate update cycle.
Realtime Video Streaming System over Narrowband Networks
Hyunmin Noh, Seunghwan Lee, Jeung Won Choi, Donghyun Kim, Kyungwoo Kim, Yunsoo Ko, Sangheon Shin, Hyungjun Kim, Hwangjun Song
http://doi.org/10.5626/JOK.2020.47.9.885
In this paper, we propose a real-time video streaming system over narrow networks that provides high-quality video services. The suggested system uses the raptor code, a forward error correction code, to support the reliable and stable data transmission in the narrowband networks. Also, the proposed system adaptively controls the raptor parameters (source symbol size, the number of source symbols, and code rate) according to the narrow network condition and the remaining buffer status. The proposed system is fully implemented on android devices and examined by using a real-time video transmission. Experimental results showed that the proposed system provides high-quality streaming services over the narrowband networks.
Traffic Steering System with Dual Connectivity for Video Streaming Services
Gi Seok Park, Hyunmin Noh, Jae Jun Ha, Hyung Jun Kim, Sang Heon Shin, Dong Hyun Kim, Jong Hwan Ko, Jeung Won Choi, Hwangjun Song
http://doi.org/10.5626/JOK.2019.46.6.577
In this paper, we propose a traffic steering system with dual connectivity to provide stable video streaming services for users by steering portion of the macrocell traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of video quality by allocating the radio resource of the macro base station. The user data flow is divided into two channels toward the macro base station and the small cell AP, and the users receive their data from both. In the proposed system, the fountain code is adopted to overcome practical issues in the dual connectivity. Moreover, the SDN is employed not only to rapidly react to time-varying network condition, but also to control network resources efficiently. The proposed system is implemented using NS-3. The simulation results show that the proposed system can achieve much better performance compared with existing traffic steering algorithms.
Link Performance Analysis of LoRa for Real-time Information Gathering in Maritime Conditions
Jaeho Shin, Junyeong Lim, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2018.45.3.303
LoRaWAN(Long Range Wide Area Network) is a standard for low-power, long-range, low-speed communication as announced in the LoRa Alliance. LoRaWAN addresses the physical layer and medium access control layer and the technology used in the physical layer is referred to as LoRa. LoRa can be used for remote monitoring and remote control in maritime conditions. However, unlike land, marine environment is not only difficult to construct an infrastructure for service provision, but also difficult to analyze LoRa performance in maritime. In this study, we construct an infrastructure using cloud platform and analyze LoRa link performance in maritime conditions.
Performance Analysis of LoRa(Long Range) according to the Distances in Indoor and Outdoor Spaces
Junyeong Lim, Jaemin Lee, Donghyun Kim, Jongdeok Kim
http://doi.org/10.5626/JOK.2017.44.7.733
LPWAN(Low Power Wide Area Network) technology is M2M (Machine to Machine) networking technology for the Internet of Things. The technology is designed to support low-power, long-distance and low-speed communications that are typical of LoRaWAN(Long Range Wide Area Network). To exchange inter-object information using a LoRaWAN, the link performances for various environments must be known. however, active performance analysis research that is based on an empirical environment is nonexistent. Therefore, this paper empirically evaluates the performance of the LoRa (Long Range) link, a physical communication technology of the LoRaWAN for various variables that may affect the link quality in indoor and outdoor environments. To achieve this, a physical performance monitoring system was designed and implemented. A communication experiment environment was subsequently constructed based on the indoor and outdoor conditions. The SNR(Signal to Noise Ratio), RSSI(Received Signal Strength Indication), and the PDR(Packet Delivery Ratio) were evaluated.
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