• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.126-137
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• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.138-149
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• 2023

In geoscience and engineering the geological characteristics of sediment strata is crucial and possible if reliable borehole logging and seismic data are available. To investigate the characteristics of the shallow strata in the Korea Strait, laboratory sonic logs were obtained from deep borehole data and seismic section. In this study, we integrated and analyzed the sonic log data obtained from the drilling core (down to a depth of 200 m below the seabed) and multichannel seismic section. The correlation value was increased from 15% to 45% through time-depth conversion. An initial model of P-wave impedance was set, and the results were compared by performing model-based, band-limited, and sparse-spike inversions. The derived P-impedance distributions exhibited differences between sediment-dominant and unconsolidated layers. The P-impedance inversion process can be used as a framework for an integrated analysis of additional core logs and seismic data in the future. Furthermore, the derived P-impedance can be used to detect shallow gas-saturated regions or faults in the shallow sediment. As domestic deep drilling is being performed continuously for identifying the characteristics of carbon dioxide storage candidates and evaluating resources, the applicability of the integrated inversion will increase in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.945-953
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• 2018

In this paper, we use 3-D LIDAR for obstacle detection and avoidance maneuver for autonomous unmanned operation. It is aimed to avoid obstacle avoidance in unmanned water under marine condition using only single sensor. 3D lidar uses Quanergy's M8 sensor to collect surrounding obstacle data and includes layer information and intensity information in obstacle information. The collected data is converted into a three-dimensional Cartesian coordinate system, which is then mapped to a two-dimensional coordinate system. The data including the obstacle information converted into the two-dimensional coordinate system includes noise data on the water surface. So, basically, the noise data generated regularly is defined by defining a hypothetical region of interest based on the assumption of unmanned water. The noise data generated thereafter are set to a threshold value in the histogram data calculated by the Vector Field Histogram, And the noise data is removed in proportion to the amount of noise. Using the removed data, the relative object was searched according to the unmanned averaging motion, and the density map of the data was made while keeping one cell on the virtual grid map. A polar histogram was generated for the generated obstacle map, and the avoidance direction was selected using the boundary value.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.1-19
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• 2023

Bikes have recently emerged as an alternative to carbon neutrality. To understand the demand for public bikes, we endeavored to estimate travel frequency of public bike by considering the intermediate stops. Using the GPS trajectory data of 'Ttareungyi', a public bike service in Seoul, we identified a stay point and estimated travel frequency reflecting population, land use, and physical characteristics. Application of map matching and a stay point detection algorithm revealed that stay point appeared in about 12.1% of the total trips. Compared to a trip without stay point, the trip with stay point has a longer average travel distance and travel time and a higher occurrence rate during off-peak hours. According to visualization analysis, the stay points are mainly found in parks, leisure facilities, and business facilities. To consider the stay point, the unit of analysis was set as a hexagonal grid rather than the existing rental station base. Travel frequency considering the stay point were analyzed using the Zero-Inflated Negative Binomial (ZINB) model. Results of our analysis revealed that the travel frequency were higher in bike infrastructure where the safety of bike users was secured, such as 'Bikepath' and 'Bike and pedestrian path'. Also, public bikes play a role as first & last mile means of access to public transportation. The measure of travel frequency was also observed to increase in life and employment centers. Considering the results of this analysis, securing safety facilities and space for users should be given priority when planning any additional expansion of bike infrastructure. Moreover, there is a necessity to establish a plan to supply bike infrastructure facilities linked to public transportation, especially the subway.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.30-38
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• 2023

As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.40-48
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• 2023

In this paper, a sensor-based monitoring system was established to analyze the long-term behavioral characteristics of the caisson quay wall, a representative structural type in port facilities. Data was collected over a period of approximately 10 months. Based on existing literature, anomalous behaviors of port facilities were classified, and a measurement system was selected to detect them. Monitoring systems were installed on-site to periodically collect data. The collected data was transmitted and stored on a server through LTE network. Considering the site conditions, inclinometers for measuring slope and crack meters for measuring spacing and settlement were installed. They were attached to two caissons for comparison between different caissons. The correlation among measured data, temperature, and tidal level was examined. The temperature dominated the spacing and settlement data. When the temperature changed by approximately 50 degrees, the spacing changed by 10 mm, the settlement by 2 mm, and the slope by 0.1 degrees. On the other hand, there was no clear relationship with tidal level, indicating a need for more in-depth analysis in the future. Based on the characteristics of these collected database, it will be possible to develop algorithms for detecting abnormal states in gravity-type quay walls. The acquisition and analysis of long-term data enable to evaluate the safety and usability of structures in the event of disasters and emergencies.