• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.449-457
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• 2001

As XML has become an emerging standard for information exchange on the World Wide Web, it has gained attention in database communities to extract information from XML seen as a database model. XML queries are based on regular path queries, which find objects reachable by given regular expressions. To answer many kinds of user queries, it is necessary to evaluate queries that have multiple regular path expressions. However, previous work such as query rewriting and query optimization in the frame work of semistructured data has dealt with a single regular expression. For queries that have multiple regular expressions we suggest a two phase optimizing technique: 1. query rewriting using views by finding the mappings from the view's body to the query's body and 2. for rewritten queries, evaluating each query conjunct and combining them. We show that our rewriting algorithm is sound and our query evaluation technique is more efficient than the previous work on optimizing semistructured queries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.543-547
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• 2004

Visualization of construction process simulation and physical modeling were considered to overcome the limitations of current graphical simulation. The output of discrete-event simulation programs which are the most common mathematical statistical simulation tool for construction processes were analyzed for the visualization of earthmoving process that dealing with objects without fixed. Object-oriented models for equipment, material and work environments were devised to effectively visualize the numerical simulation results of the working time, the queuing time as well as the amount resources etc. The oscillation of the crane's cable and the lifted material that should be considered to rationally modeled and simulated by construction graphical simulation. The derived equation of motion was solved by numerical analysis procedure. Then obtained results was used for physical modeling.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.127-133
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• 2009

To ensure access to rapidly growing video collection, video indexing is becoming more and more essential. A database for video should be build for fast searching and extracting the accurate features of video information with more complex characteristics. Moreover, video indexing structure supports efficient retrieval of interesting contents to reflect user preferences. In this paper, we propose semantic video retrieval method based on user preference. Unlikely the previous methods do not consider user preferences. Futhermore, the conventional methods show the result as simple text matching for the user's query that does not supports the semantic search. To overcome these limitations, we develop a method for user preference analysis and present a method of video ontology construction for semantic retrieval. The simulation results show that the proposed algorithm performs better than previous methods in terms of semantic video retrieval based on user preferences.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.4
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• pp.7-16
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• 2005

Computer applications for engineering design evolve rapidly. Many design frameworks were developed by the simulation based systems so that organizations could achieve significant benefits due to cost reduction in designing. However, today’s transient design issue requires being adaptable to more complicated and atypical problems. In this paper the Multidisciplinary Language Runtime (MLR) design framework is developed. The MLR provides flexible and extensible interface between analysis modules and numerical analysis codes. It also supports Meta Modeling, Meta Variable, and XML script for atypical design formulation. By applying object-oriented design scheme to implement abstractions of the key components required for iterative systems analyses, the MLR provides flexible and extensible problem-solving environment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.32-37
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• 2008

To ensure access to rapidly growing video collection, video indexing is becoming more and more important. In this paper, video ontology system for retrieving a video data based on a scene unit is proposed. The proposed system creates a semantic scene as a basic unit of video retrieval, and limits a domain of retrieval through a subject of that scene. The content of semantic scene is defined using the relationship between object and event included in the key frame of shots. The semantic gap between the low level feature and the high level feature is solved through the scene ontology to ensure the semantic-based retrieval.

• Journal of Sasang Constitutional Medicine
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• v.11 no.1
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• pp.311-327
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• 1999

Lee, Je-ma proposed ways like Chehyungkisang to judge each constitution, so, on the basis of this, we can judge constitution in various ways. Such a theory is based on behavior determinism's way of thinking of Lee, Je-ma. We can know this that form is not recognized as an object reflecting image, but a subjective concept from Tukyonyodun, Yimokbiku(ears, eyes, nose and mouth), Hameokjebok of Sungmyong Theory. Lee, Je-ma thought each part of human body has not only physical function but also complex temperative function. Putting this consideration and Jangbu Theory describing human body directly, together, it can be said that these all have an established theory on Chehyungkisang of constitution judgement. Thus, the following hypotheses are given. From Sadan Theory and Hwakchung Theory, strength of Jangbu of Sasangin is Pe>Bi>Shin>Kan in Taeyangin and Bi>Pe>Kan>Shin in Soyangin and Kan>Shin>Bi>Pe> in Taeumin and Shin>Kan>Pe>Bi in Soumin. The concept of Shinkihyuljung is related with creation of form and spirit of each Jangkuk and Aenoheerak(sorrow, anger, joy, pleasure). From this viewpoint, Sasangin can be classified into; Taeyangin into Shinkijunghyul type, Taeumin into Hyuljungkishin type, Soumin into Junghyuishinki type. Introduced a fixed way to explain of each constitution according to this strength relationship. I hope more lively discussions on Constitutional Medicine will be continued based on this attempt.