• Journal of Service Research and Studies
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• v.12 no.2
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• pp.23-37
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• 2022

Based on the service scenario proposed by the existing Kim Tae-wan (2018) who can safely evacuate inmates with the help of a mobile application linked to a fire detection system in the event of a fire, the final purpose of this study is to develop the scenario by incorporating more realistic scenarios with mobile stimuli that can help them escape or act through the Delph In addition, to make the scenarios produced more realistic considering the structure and copper lines of a typical building, expert scenario verification and Delphi technique were applied to exclude unnecessary or impractical aspects of the existing scenarios. The results of the second Delphi survey showed that the primary psychology that could be seen at the time of the fire alarm were doubts, safety concerns and alarm, and the results of the second Delphi survey were analyzed, and the satisfaction of the content adequacy (CVR), convergence, and consensus was derived. Finally, this was applied to create a scenario in which a mobile application was assisted to evacuate the fire response phase. This study will allow the use of methods to increase the evacuation rate of those who are in the event of a fire.

• 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.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• 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.

• Korean Journal of Environmental Agriculture
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• v.28 no.1
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• pp.1-8
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• 2009

This study was aimed at assess environmental conservation functions by analyzing the change of land use areas in agricultural fields between 1999 and 2006, and comparing land surface temperature distribution between 1994 and 2006 in Yongin city. Land use maps of Yongin city were obtained from soil maps for 1999, Quickbird satellite images(less than 1 m) and parcel map for 2006. The land use area for Yongin city was in the order of forest > paddy field > upland > residence & building in 1999, and forest > residence & building > paddy field > upland in 2006. Decrease of paddy and upland fields reduced 34% and 41% of the capability of agricultural multifunctionality as to environment including flood control, groundwater recharge, and air cooling. Land surface temperature(LST) was derived from Landsat TM thermal infrared band acquired in September of 1994 and 2006 and classified into three grades. The results impplied that green vegetation in agricultural field and forest play an important role to reduce land surface temperature in warm season.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of environmental and Sanitary engineering
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• v.15 no.4
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• pp.59-77
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• 2000

This study was carried out to investigate on several factors, which contaminative the water quality through the water pipe during feeding water, in 42 largescaled apart-ments(total 84 cases) and assayed the Volatile Organic Compounds(VOCs) and concen-tration of heavy metals that inflow and outflow in reservior water in Jeonnam area(Mokpo, Suncheon, Yeosu) from January 1999 to December 1999. The results obtained were summarized as follows ; 1. The quality of the water pipe composition in the order of frequency in the quality of water pipes were Copper(45.2%)> Zinc(38.9%)> Stainless steel(9.5%)> PVC(4.8%)> PM(2.4%) in observing 42 sites. All of the drain pipes were used the cast iron quality. 2. The result of certification curve from 12 items(17kind) of VOCs was $1.0-4.0{\mu{g}}/{\ell}$ range, a coefficient of correlation was shown 0.99 over. A MDL of each substance range was within $0.1-1.0{\mu{g}}/{\ell}$. 3. The result of the assay, 5 kinds(Viny chloride, Dichloromethane, Ethylbenzene, M,P-xylene, Styrene) of the VOCs of 14 kinds was not detected and the other items were detected slightly. The detection rate of one item and over in total VOCs samples, were 25.9% in inflow and 27.9% in outflow. And frequency of detect in inflow/outflow of THM(Chloroform, Bromodichloro-methane, Dibromochloromethane, Bromoform) were shown higher than 94.1%, 97.0% each stages. It comes to the conclusion that all of the samples were reason able for drinking water standards. 4. The coefficient of correlation were reasonable, it shown 0.999 over in $0.1-1.0{\mu{g}}/{\ell}$ of a measuring range conditions of 4kinds in organic substance(Zn, Cu, Fe, Mn). 5. The results were showed suitability in 78 cases(92.9%) and unsuitability in 6 cases (7.1%), in 84 cases of in organic substances. Compare to inflow stage, mean concentrations of heavy metal, were increased slightly in Zn, Cu, Fe except Mn than outflow stage. The result of the mean concentration in organic substance inflow and outflow in the apartment water tank using Pair-compared T-test, in 95% reliance index, were $0.179mg/{\ell}(0.151-0.307mg/{\ell})$ in Zinc, $0.136mg/{\ell}(0.113-0.230mg/{\ell})$ in Copper, $0.052mg/{\ell}(0.048-0.098mg/{\ell})$ in Fe, and there was a bit growing tendency but there was no differece in Mn. 6. The mean concentration of Copper which used Cu pipe as a water supply pipe in apartment were $0.216mg/{\ell}(0.161-0.338mg/{\ell})$ in case of the Zine pipe were $0.286mg/{\ell}(0.204-0.435mg/{\ell})$. It shows that the detection rate was more higher than the other material used in Cu or Zn as the water supply pipe. We supposed to Cu and Zn substance were gushing out water supply pipe.

• 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.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.