• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.89-101
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• 2022

The study was conducted in Erhe Park, a neighborhood park in Dali City, China, to derive the influence of environmental factors in the park on the elderly's satisfaction and to analyze the satisfaction in order to provide improvement recommendations. First, the evaluation indicators were selected, by referencing previous research into the elderly's evaluation of external spatial environments and the influence of various factors was determined from the questionnaire and factor analysis. Second, a fuzzy comprehensive evaluation was conducted to understand the satisfaction of the elderly, and then an Importance-Performance Analysis(IPA) analysis was conducted to develop improvement recommendations. Results of the factor analysis showed that park's environmental factors were grouped into four categories: facility suitability and comfort, safety and convenience of use, natural environment comfort, and parkway convenience. Based on this, the overall satisfaction rating for the environmental factors in Erhe Park was between satisfactory and average. The natural environment comfort was rated satisfactory, the facility suitability and comfort, and convenience and safety were rated average, and parkway convenience was rated near to satisfaction. The IPA analysis revealed that the suitability of rest facilities, suitability of convenience facilities, facility management status, safety of entrances and exits, and nighttime lighting facilities were items of high importance but low satisfaction that needed to be improved. The results of this study can be utilized as a guide for future building or readjustment of senior-friendly parks, and they are critical for increasing senior-friendly park satisfaction.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.469-479
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• 2021

As a material for preventing spalling of concrete, the effectiveness of PP fiber has already been confirmed. However, it is necessary to consider the maximum temperature that occurs during a fire, and to solve the mixing problem and the strength reduction problem that occur depending on the mixing amount. In this study, the fire resistance performance of tunnel segment linings according to the PP fiber content and air volume under the RABT fire scenario was investigated. As a result, no spalling or cross-sectional loss occurred in all test specimens, and when the PP fiber content was small, the maximum temperature was relatively high and the maximum temperature arrival time was also fast. On the other hand, no trend was found for the maximum temperature and arrival time according to the difference in air volume. In the internal temperature distribution results for the PP fiber mixing amount of 0.75, 1.0, 1.5, and 2.0 kg/m³, the results of 0.75 and 1.0 kg/m³ showed similar temperature distribution, and the results of 1.5 and 2.0 kg/m³ were similar. It was confirmed that the internal temperature distribution tends to decrease at the same depth when the amount of PP fiber mixed is large, and it was confirmed that a remarkable difference occurred from the results of 1.0 kg/m³ and 1.5 kg/m³ of PP fiber mixed amounts.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.201-210
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• 2022

Expanded polystyrene (EPS) is the most commonly used fresh food refrigeration insulation in Korea and Europe. Moreover, as the use of disposable packaging materials has increased significantly along with non-face-to-face delivery services since the COVID-19 crisis, social issues related to waste disposal are also being raised. Therefore, in this study, the life cycle of EPS boxes for fresh food is focused on the factors that have a large difference between incineration and landfill including recycling in Europe and Korea in the disposal process after use, and raw materials and energy in the manufacturing process, which account for a large portion of the environmental impact value. We tried to compare the environmental impact of evaluation. Overall, the raw material production stage, box manufacturing stage, and packaging stage have similar processes in Europe and Korea, but unlike Europe, Korea, which lacks landfills and incineration facilities, has focused on expanding the recycling rate. It was necessary to do an environmental impact assessment. Data affecting the environment were derived based on 2019 and 2020 data for Korea and 2017 and 2020 data for Europe. In order to predict the future environmental impact assessment, assumptions about the disposal rate in 2025 and 2030 were introduced and evaluated. As a result of this study, it was found that the raw material production stage of EPS boxes, which have similar processes in both Korea and Europe, has the greatest effect on the global warming effect of Korean EPS boxes. However, Korea, which has a relatively high recycling rate in the disposal process compared to incineration and landfill, showed better environmental performance than Europe in most impact indicators except freshwater eutrophication. In particular, Korea has increased the overall recycling rate compared to Europe by replacing various recyclable materials such as building materials and sundries with XPS (extruded polystyrene) recycled materials. In conclusion, it was found that increasing the recycling rate rather than incinerating and landfilling EPS boxes for fresh food in the domestic EPS industry has relatively less environmental load compared to Europe.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

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.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.93-100
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• 2024

Pore water freezes in low-temperature compaction, which leads to different compaction characteristics compared to room temperature conditions. In regions like Alberta, Canada, where organic soils are prevalent, compaction performance is influenced by the high water retention and compressibility of organic soils, as well as their sensitivity to freezing and thawing. Alberta's strict environmental regulations demand the reuse of excavated soil for backfill, and the long winter season creates challenging conditions for civil engineering projects. In this study, a laboratory compaction test was conducted to evaluate the low-temperature compaction characteristics of organic soils with varying organic content. The results indicate that the optimum moisture content increases as the organic content increases, and the maximum dry unit weight decreases by up to 21.9%. In addition, under temperature conditions below -4℃, no optimum moisture content was observed, and the dry unit weight decreased as the moisture content increased.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.