• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1714-1729
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• 2018

Change detection of remote sensing images is a profound challenge in the field of remote sensing image analysis. This paper proposes a novel change detection method for bitemporal remote sensing images based on feature fusion and fuzzy c-means (FCM). Different from the state-of-the-art methods that mainly utilize a single image feature for difference image construction, the proposed method investigates the fusion of multiple image features for the task. The subsequent problem is regarded as the difference image classification problem, where a modified fuzzy c-means approach is proposed to analyze the difference image. The proposed method has been validated on real bitemporal remote sensing data sets. Experimental results confirmed the effectiveness of the proposed method.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.609-615
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• 2001

This study proposes analysis and evaluation method of time series ultrasonic signal using attractor analysis for fusion joint part of polyethylene piping. Quantitatively characteristics of fusion joint part is analysed features extracted from time series. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics. These differences in characteristics of fusion joint part enables the evaluation of unique characteristics of fusion joint part. In quantitative fractal feature extraction, feature values of 4.291 in the case of debonding and 3.694 in the case of bonding were proposed on the basis of fractal dimensions. In quantitative quadrant feature extraction, 1,306 point in the case of bonding(one quadrant) and 1,209 point(one quadrant) in the case of debonding were proposed on the basis of fractal dimensions. Proposed attractor feature extraction can be used for integrity evaluation of polyethylene piping material which is in case of bonding or debonding.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.33-40
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• 2000

Computer Aided Process Planning (CAPP) plays a key role by linking CAD and CAM. Given CAD data of a part, CAPP has to recognize manufacturing features of the part. Despite the long history of research on feature recognition, its research results have rarely been transferred into industry. One of the reasons lies in the separation of feature recognition and process planning. This paper proposes to integrate the two activities through AI techniques, and presents efforts for manufacturable feature recognition, setup minimization, feature dependency construction, and generation of an optimal machining sequence.

• 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.

• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.488-498
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• 2008

Soil and Water Assessment Tool (SWAT) model have been widely used in simulating hydrology and water quality analysis at watershed scale. The SWAT model extracts topographic feature using the Digital Elevation Model (DEM) for hydrology and pollutant generation and transportation within watershed. Use of various DEM cell size in the SWAT leads to different results in extracting topographic feature for each subwatershed. So, it is recommended that model users use very detailed spatial resolution DEM for accurate hydrology analysis and water quality simulation. However, use of high resolution DEM is sometimes difficult to obtain and not efficient because of computer processing capacity and model execution time. Thus, the SWAT Topographic Feature Extraction Error (STOPFEE) Fix module, which can extract topographic feature of high resolution DEM from low resolution and updates SWAT topographic feature automatically, was developed and evaluated in this study. The analysis of average slope vs. DEM cell size revealed that average slope of watershed increases with decrease in DEM cell size, finer resolution of DEM. This falsification of topographic feature with low resolution DEM affects soil erosion and sediment behaviors in the watershed. The annual average sediment for Soyanggang-dam watershed with DEM cell size of 20 m was compared with DEM cell size of 100 m. There was 83.8% difference in simulated sediment without STOPFEE module and 4.4% difference with STOPFEE module applied although the same model input data were used in SWAT run. For Imha-dam watershed, there was 43.4% differences without STOPFEE module and 0.3% difference with STOPFEE module. Thus, the STOPFEE topographic database for Soyanggang-dam watershed was applied for Chungju-dam watershed because its topographic features are similar to Soyanggang-dam watershed. Without the STOPFEE module, there was 98.7% difference in simulated sediment for Chungju-dam watershed for DEM cell size of both 20 m and 100 m. However there was 20.7% difference in simulated sediment with STOPFEE topographic database for Soyanggang-dam watershed. The application results of STOPFEE for three watersheds showed that the STOPFEE module developed in this study is an effective tool to extract topographic feature of high resolution DEM from low resolution DEM. With the STOPFEE module, low-capacity computer can be also used for accurate hydrology and sediment modeling for bigger size watershed with the SWAT. It is deemed that the STOPFEE module database needs to be extended for various watersheds in Korea for wide application and accurate SWAT runs with lower resolution DEM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1214-1218
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• 2006

This paper presents method to speed up processing time in construction of panoramic images. The method based on invariant feature uses image down-scaling and image edge information. Reducing image size and applying feature descriptor to image portions superimposed with edge causes to reduce the number of features and to improve processing speed. In the experiments, it was shown that the proposed method was 3.26$\sim$13.87% shorter in processing time than the exiting method fer 24-bit color images of 640$\times$480 size.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.635-641
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• 2007

The construction starts its business by receiving the order from client and owner. The client presents requirements and related information so that the desired result can come out while the designer and the builder express and implement the building according to set objectives and goals of project based on information on project environment and presented requirements. Because the construction project makes decisions on such objectives at its early stage and previous stage becomes the situation for decision making of next stage as the computation to implement decisions get performed. Accordingly, this study has mentioned necessity of requirement analysis and systematization of construction project, analyzed the work of construction's early stage and resented a plan for objective of construction project requirement definition model in which requirements engineering has been applied and using it while making decisions on designing.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.464-471
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• 2009

Robot and application of IT skill of construction industry are slow comparatively than another thing industry by the feature. This research proposes progress management and real time information gathering through construction automation and RFID focused on steel structure construction. Building for RTPM system, must consider various variables and surrounding situation in construction field and it is the most important and difficult matter that draw right requirement and grasp relation between this requirements to accomplish one suitable task considering these environment. Therefore, in this study analyzes requirement and target for RTPM system based on scenario that is easy to draw requirement and apply this to use case model. Presented method suggests that represent relation between goals and way that refines goal systematically from requirement of RTPM system. And it could express for visualization through the Way that attaches nonfunctional elements of system with system internal goal.