• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.233-239
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• 2021

Recently, the COVID-19 virus has caused people to stay indoors longer without going out. As a result of this, people's activity decreased sharply, and their weight gained. So people became more interested in health. Home training can be an alternative method to solve this problem. Accordingly, To find out the trends of home training, we collected articles from December 1, 2019, to November 30, 2020, using the news provided by BIG KINDS, a news analysis system. We analyzed frequency analysis, relational analysis according to weighting, and related word analysis with the program using the algorithm developed by BIG KINDS. In conclusion, first, it was found that home training is led by technology and the emergence of artificial intelligence. Second, it can be assumed that people mainly do home training using content and video services related to mobile carriers. Third, people had a high preference for Pilates in the sports category. It can be seen that the number of patent applications increased as the demand for exercise products related to Pilates increased. In the next study, we expect that this study will be used as primary data for various big data studies by supplementing the research methodology and conducting various analyses.

• 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.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.217-230
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• 2022

The adequate control of TBM face pressure is of vital importance to maintain face stability by preventing face collapse and surface settlement. An EPB shield TBM excavates the ground by applying face pressure with the excavated soil in the pressure chamber. One of the challenges during the EPB shield TBM operation is the control of face pressure due to difficulty in managing the excavated soil. In this study, the face pressure of an EPB shield TBM was predicted using the geological and operational data acquired from a domestic TBM tunnel site. Four machine learning algorithms: KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), and XGB (eXtreme Gradient Boosting) were applied to predict the face pressure. The model comparison results showed that the RF model yielded the lowest RMSE (Root Mean Square Error) value of 7.35 kPa. Therefore, the RF model was selected as the optimal machine learning algorithm. In addition, the feature importance of the RF model was analyzed to evaluate appropriately the influence of each feature on the face pressure. The water pressure indicated the highest influence, and the importance of the geological conditions was higher in general than that of the operation features in the considered site.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.554-561
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• 2022

Technology development related to maritime autonomous surface ships (MASS) is actively progressing around the world. However, since there are still many technically unresolved problems such as communication, cybersecurity, and emergency response capabilities, it is expected that it will take a lot of time for MASS to be commercialized. In this study, we proposed a ship group navigation system in which one leader ship and several follower ship are grouped into one group. In this system, when the leader ship begins to navigate, the follower ship autonomously follows the path of the leader ship. For path following, PD (proportional-derivative) control is applied. In addition, each ship navigates in a straight line shape while maintaining a safe distance to prevent collisions. Speed control was implemented to maintain a safe distance between ships. Simulations were performed to verify the ship group navigation system. The ship used in the simulation is the L-7 model of KVLCC2, which has related data disclosed. And the MMG (Maneuvering Modeling Group) standard method proposed by the Japan Society of Naval Architects and Ocean Engineering (JASNAOE) was used as a model of ship maneuvering motion. As a result of the simulation, the leader ship navigated along a predetermined route, and the follower ship navigated along the leader ship's path. During the simulation, it was found that the three ships maintained a straight line shape and a safe distance between them. The ship group navigation system is expected to be used as a navigation system to solve the problems of MASS.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.1-14
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• 2022

This study tried to suggest crisis management compliance to prevent personal information infringement accidents that may occur in the process because the data including personal information is being processed in the artificial intelligence (AI) service process. To this end, first, the AI service provision process is divided into 3 processes such as service planning/data design and collection process, data pre-processing and purification process, and algorithm development and utilization process. And 3 processes are subdivided into 9 stages following to personal information processing stages to infringe personal information. All processes were investigated with literature and experts' Delphi. Second, the investigated personal information infringement factors were selected through FGI, Delphi, etc. for experts. Third, a survey was conducted with experts on the severity and possibility of each personal information infringement factor, and the validity and adequacy of the 94 responses were verified. Fourth, to present appropriate risk management compliance for personal information infringement factors in AI services, a method for calculating the risk level of personal information infringement is prepared by utilizing the asset value of personal information, personal information infringement factors, and the possibility of infringement accidents. Through this, the countermeasures for personal information infringement incidents were suggested according to the scored risk level.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.274-286
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• 2022

Multi-object tracking has been studied for a long time under computer vision and plays a critical role in applications such as autonomous driving and driving assistance. Multi-object tracking techniques generally consist of a detector that detects objects and a tracker that tracks the detected objects. Various publicly available datasets allow us to train a detector model without much effort. However, there are relatively few publicly available datasets for training a tracker model, and configuring own tracker datasets takes a long time compared to configuring detector datasets. Hence, the detector is often developed separately with a tracker module. However, the separated tracker should be adjusted whenever the former detector model is changed. This study proposes a system that can train a model that performs detection and tracking simultaneously using only the detector training datasets. In particular, a Siam network with augmentation is used to compose the detector and tracker. Experiments are conducted on public datasets to verify that the proposed algorithm can formulate a real-time multi-object tracker comparable to the state-of-the-art tracker models.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.71-76
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• 2023

Recently, in order to store and manage big data, research and development of a high-performance storage system capable of stably accessing large data have been actively conducted. In particular, storage systems in data centers and enterprise environments use large amounts of SSD (solid state disk) to manage large amounts of data. In general, SSD uses FTL(flash transfer layer) to hide the characteristics of NAND flash memory, which is a medium, and to efficiently manage data. However, FTL's algorithm has a limitation in using DRAM more to manage the location information of NAND where data is stored as the capacity of SSD increases. Therefore, this paper introduces FTL policies that apply virtual memory to reduce DRAM resources used in FTL. The virtual memory-based FTL policy proposed in this paper manages the map data by using LRU (least recently used) policy to load the mapping information of the recently used data into the DRAM space and store the previously used information in NAND. Finally, through experiments, performance and resource usage consumed during data write processing of virtual memory-based FTL and general FTL are measured and analyzed.