• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.172-179
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• 2006

In this paper, we present a practical place and object recognition method for guiding visitors in building environments. Recognizing places or objects in real world can be a difficult problem due to motion blur and camera noise. In this work, we present a modeling method based on the bidirectional interaction between places and objects for simultaneous reinforcement for the robust recognition. The unification of visual context including scene context, object context, and temporal context is also. The proposed system has been tested to guide visitors in a large scale building environment (10 topological places, 80 3D objects).

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.249-254
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• 2003

Object caching is a desirable feature to improve the both scalability and performance of distributed application systems for information infrastructure, the information management system leveraging the power of network computing. However, in order to exploit such benefits, we claim that the following problems: cache server placement, cache replacement, and cache synchronization, should be considered when designing any object cache system. We are under developing DOC: a Distributed Object Caching, as a part of building our information infrastructures. In this paper, we show how each problem is inter-related, and focus to highlight how we handle cache server deployment problem

• Journal of KIBIM
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• v.12 no.4
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• pp.70-79
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• 2022

As BIM(Building Information Modeling) technology advances in architecture around the world, projects and industries using BIM are increasing. Unlike previous developments that were limited to buildings, BIM is now spreading to other fields such as civil engineering and electricity. In architecture, BIM is used in the entire process from design to maintenance of a building, and IFC(Industry Foundation Classes), a neutral format with interoperability, is used as an open BIM format. Since firefighting requires intuitive 3D models for evacuation and fire simulations, BIM models are desirable. However, due to the BIM model, which was developed centered on building objects, there are no objects and specific properties for fire evacuation in the IFC scheme. Therefore, in this study, when adding a new object in the firefighting area to the IFC schema, the IFC interoperability is not broken and the building WBS(Work Breakdown Structure) is analyzed with a hierarchical system similar to the IFC format to define the scope for a new object and the firefighting part within of the building WBS to derive a firefighting HBS(Hierarchy Breakdown Structure) with the extension of the object-oriented IFC file. And according to HBS, we propose an IFC schema extension method. It is a methodology that allows BIM users to instantly adapt the IFC schema to their needs. Accordingly, the methodology derived from this study is expected to be expanded in various areas to minimize information loss from IFC. In the future, we will apply the IFC extension methodology to the actual development process using HBS to verify that it is actually applicable within the IFC schema.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3340-3349
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• 1999

Supporting object group on distributed object system give merits such as load balancing, fault tolerance and high availability. In this paper, we describe a CORBA ORB that has been designed to support object group based on active replication. The ORB supports the operational model in which it uses the IIOP for communication between client and server and total ordered multicast protocol for consistency control among group members. And through extension of ORB, it provides functions required for support of object group. Since it provides transparency of object replication, the ORB is interoperable with the existing CORBA products. It make possible for existing server application to be easily extended to application supporting object group as adding interface functions which should be used for building applications is minimized. A prototype is implemented, and performance of the replicated object group is tested and compared with a single object invocation.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.97-105
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• 2015

Building information modeling (BIM)-based construction projects have increased and become more varied, and as such the management of BIM-based facility information is also increasingly important for facility maintenance. Information management, and specifically product data mapping, however, has some problems in the area of manual data entry and does not adequately consider the exchange requirements of facility maintenance. Therefore, it is necessary to introduce a method to improve the management of composite object information for BIM-based facility maintenance so that it can handle construction operation building information exchange (COBie) data for a composite object. Therefore, we present a method to map COBie data to related materials of a composite object. This research contributes to increasing the efficiency and accuracy of the required information mapping between a building model and product data using a BIM library through optimal BIM data adoption. Moreover, it allows for the creation and management of specific product data at the design development phase.

• Journal of the Korea Institute of Building Construction
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• v.8 no.4
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• pp.71-77
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• 2008

Earthwork is very equipment-intensive task and researches related to automated excavation have been conducted. There is an issue to secure the safety for an automated excavating system. Therefore, this paper focuses on how to improve safety for semi- or fully-automated backhoe excavation. The primary objective of this research is to develop the core technology for automated object detection in excavation work. In order to satisfy the research objective, a diverse sensing technologies are investigated and analysed in terms of functions, durability, and reliability. The authors developed detecting algorithm for the objects using laser sensor and verified its performance by several tests. The results of this study would be the basis for developing the automated object detection system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.923-926
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• 2003

In this paper we propose a scheme to generate DTM and detect buildings on DSM generated from LIDAR data. Two stages are performed. The first stage is to perform object segmentation by using two morphology operations namely, flattening and H-Dome transformation. After filtering out the object points above the ground, we used the non-object points to generate DTM. The second stage is to detect buildings from the objects by analyzing differential slopes. The test data is in raster form with 1m spacing around Hsin-Chu Scientific Area in Taiwan. The mean error is -0.16m and the RMSE is 0.45m for DTM generation. The successful rate for building detection is 87.7%.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.