• Korean Journal of Construction Engineering and Management
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• v.20 no.3
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• pp.39-45
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• 2019

Crew productivity in the construction industry is an important indicator of soundness and efficiency of work process, since all works in a site are conducted as groups of people. A survey was conducted in order to reveal importance and performance of major management factors affecting crew productivity in road bridge construction site using IPA. As a result of the analysis, it was found that 'Construction equipment' and 'Human resource related' factors among five major-categories are most important but with low performance. Furthermore, from another analysis with 27 factors of sub-categories, it is revealed that factors needed sustained attention are four, namely 'Just-in-time machinery delivery', 'Formation of a crew members', 'Skill of workers', and 'Site control and management', whereas those needed much more improvement are five, such as 'Machinery performance', 'Clearity of Design', 'Clearity of shop drawing', 'Timing of work instruction and approval', and 'Clearity of work instruction'. Findings from this study will enable road agencies as well as road construction experts to enhance crew productivity in a site.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.1
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• pp.3-14
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• 2018

An apartment complex is a building use with great potential to contribute to solving problems related to urban ecological environment and climate change. The first goal of this study is to grasp the current situation of application and limitations of the ecological area rate, which is a representative evaluation index used to evaluate the environmental performance of the external space of an apartment complex in Green Standard for Energy and Environmental Design (G-SEED). The second goal is to propose a prototype of the evaluation index for evaluating greenhouse gas (GHG) reduction performance in order to supplement the evaluation index for the environmental performance of the external space in terms of response to climate change. We analyzed 43 cases of apartment complexes certified according to G-SEED, which was enforced since July 1, 2010, and found application characteristics of each space type and the limitations of ecological area rate. We analyzed overseas green building certification systems such as LEED and BREEAM that derived implications for supplementing the limitations of ecological area rate, which is focused on the evaluation of soil and water circulation function, and set up a development direction of complementary measures. Through analysis of previous studies, relevant regulations and standards, and technical documents of the manufacturer, the heat island mitigation performance of the pavement and roof surfaces of the apartment complex and the carbon uptake performance of the trees in the apartment complex was selected as parameters to yield the GHG reduction performance of the external space of the apartment complex. Finally, a quantitative evaluation method for each parameter and a prototype of the evaluation index for the GHG reduction performance were proposed. As a result of applying the prototype to an apartment complex case, the possibility of adoption and applicability as an evaluation index of G-SEED were proved.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.39-60
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• 2021

In the field of deep learning, there are many algorithms mainly after GAN in research related to generation, but in terms of generation, there are similarities and differences with art. If the generation in the engineering aspect is mainly to judge the presence or absence of a quantitative indicator or the correct answer and the incorrect answer, the creation in the artistic aspect creates a creation that interprets the world and human life by cross-validating and doubting the correct answer and incorrect answer from various perspectives. In this paper, the video generation ability of deep learning was interpreted from the perspective of collage and compared with the results made by the artist. The characteristic of the experiment is to compare and analyze how much GAN reproduces the result of the creator made with the collage technique and the difference between the creative part, and investigate the satisfaction level by making performance evaluation items for the reproducibility of GAN. In order to experiment on how much the creator's statement and purpose of expression were reproduced, a deep learning algorithm corresponding to the statement keyword was found and its similarity was compared. As a result of the experiment, GAN did not meet much expectations to express the collage technique. Nevertheless, the image association showed higher satisfaction than human ability, which is a positive discovery that GAN can show comparable ability to humans in terms of abstract creation.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.515-523
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• 2021

An integrated underground spatial map consists of underground facilities, underground structures, and ground information, and is periodically updated. In this paper, we design and implement a system for detecting and extracting only changed ground objects to shorten the map update speed. To find the changed objects, all the objects are compared, which are included in the newly input map and the reference map in the integrated map. Since the entire process of comparing objects and generating results is classified by function, the implemented system is composed of several modules such as object comparer, changed object detector, history data manager, changed object extractor, changed type classifier, and changed object saver. We use two metrics: detection rate and extraction rate, to evaluate the performance of the system. As a result of applying the system to boreholes, ground wells, soil layers, and rock floors in Pyeongtaek, 100% of inserted, deleted, and updated objects in each layer are detected. In addition, it provides the advantage of ensuring the up-to-dateness of the reference map by downloading it whenever maps are compared. In the future, additional research is needed to confirm the stability and effectiveness of the developed system using various data to apply it to the field.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.337-345
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• 2023

Monitoring river microplastics is a challenging task since it is a time-consuming and high-cost process. The use of a physical model to have a better understanding of river microplastics' behaviors can complement the challenging monitoring process. However, there have been very limited studies on modeling river microplastics. In this study, therefore, we evaluated the applicability of one commonly used river water quality model, i.e., the Water Quality Analysis Simulation Program (WASP), in simulating the microplastic concentration in the river environment. We simulated the microplastic concentration in the Anyangcheon stream using the WASP's biochemical oxygen demand (BOD) and suspended solid (SS) variables as possible surrogate variables for the microplastics. Simulation analyses indicate that the SS state variable performs better than the BOD state variable to mimic the observed concentrations of microplastics. This is because of the characteristics of each water quality parameter; the BOD variable, a biochemical indicator, is inappropriate for modeling the behaviors of microplastics, which have generally constant biochemical features. In contrast, the SS variable, which has similar physical behaviors, followed the observed patterns of the microplastic concentrations well. To build a more advanced and accurate model for simulating the microplastic concentration, comprehensive and long-term monitoring studies of the river microplastics under different environmental conditions are needed, and the unit of microplastic concentration should be carefully addressed before its modeling application.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.