• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.41 no.1
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• pp.17-26
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• 2005

There are several problems in the commercial pound net in the heavy tide ; the breaking and loss of net, steeply variation of net shape and decreasing of fishing efficiency, etc. In order to solve these problems, we introduced method of added sinker used to coastal cultivating cage of Japan and investigated the possibility of application to the Korean pound net. The results are obtained as follows; 1. In case of the upperward flow with fish court net, tension of the frame line was increased about 10${\sim}$25% than that of prototype according to the added sinker from 1.3gf to 5.2gf. The tension of A-type and B-type was similar to the case of the prototype, the tension of C-type and D-type was increased about 10${\sim}$15% than that of prototype. 2. The variation of deformed angle of fish court net was from 0$^{\circ}$ to 70$^{\circ}$ and that of the slope net was from 0$^{\circ}$ to 64$^{\circ}$ and that of the second bag net was from 0$^{\circ}$ to 46$^{\circ}$ and the depth of the second bag net was increased about 10% when the added sinker was changed from 1.3gf to 5.2gf. The depth of the first bag net and the second bag net were decreased about 50% than that of initial depth. 3. For the deformed angle of fish court net according to the attached point of the added sinker, A-type and B-type were decreased about 25% and 10% than the prototype, respectively. C-type was similar to the case of the prototype and D-type was increased about 15% than that of the prototype. The depth of slope net became deep in turn of A-type, B-type, C-type and D-type. For the depth of the second bag net, A-type, B-type, C-type and D-type were increased about 10${\sim}$15% than that of prototype. The depth of the slope net was changed from 0$^{\circ}$ to 63$^{\circ}$ and that of the second bag net was changed from 0${\sim}$44$^{\circ}$ according to the increase of velocity. 4. The optimal weight of added sinker was about 2.6${\sim}$3.6gf and the optimal attached point of added sinker was the case of C-type and D-type.

• The Journal of Society for e-Business Studies
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• v.3 no.2
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• pp.141-160
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• 1998

In order to achieve a successful introduction of CALS, an enterprise model which is a concise description of what an enterprise does is required. The enterprise model mainly consists of a process model and a data model. The process model describes processes that create, change, combine, or destroy the entities within the enterprise. Several process modeling methodologies have been proposed. Each modeling methodology requires its own modeling view point, elements, and syntax. In developing a process model for CALS, these models created at different view points are required to analyze and design a system in a broad view. This paper aims at proposing an integration methodology for a process model. Specifically, IDEF0, IDEF3, and Petri Net are considered to be integrated. An IDEF0 model describing static functions of enterprise is transformed to an IDEF3 model describing behaviour of a system with additional information. Also, the IDEF3 model is transformed to a Petri Net model. These transformations will be automatically accomplished once the additional information for the transformation is provided.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.2
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• pp.159-168
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• 2010

This paper presents a mathematical model and simulation method for investigating the performance of set net systems and fish cage systems influenced by currents and waves. Both systems consist of netting, mooring ropes, a floating collar and sinkers. The netting and ropes were considered flexible structures and the floating collar was considered an elastic structure. Both were modeled on a mass-spring model. The structures were divided into finite elements and mass points were placed at the mid-point of each element, and the mass points were connected by mass-less springs. Each mass point was subjected to external and internal forces and the total force was calculated at every integration step. An implicit integration scheme was used to solve the nonlinear dynamic system. The computation method was applied to dynamic simulation of actual systems simultaneously influenced by currents and waves in order to evaluate their practicality. The simulation results improved our understanding of the behavior of the structure and provided valuable information concerning the optimized design of set net and fish cage systems exposed to an open ocean environment.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of Bio-Environment Control
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• v.13 no.2
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• pp.102-106
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• 2004

Objective of this research was to investigate the effect of fertigation setting point such as -5, -10, -20, and -30 ㎪ on the growth and fruit quality of sweet pepper (Capsicum annuum L.) in greenhouse culture. The net $CO_2$ assimilation and transpiration rate were the lowest in the treatment of -30㎪. The pH and EC in soil solution were not severly affected by irrigation setting point and no statistical differences were observed among treatments of irrigation setting point tested. The N content of above ground plant tissue was the lowest in the treatment of -30 ㎪ and those of K, Ca, and Mg were the highest in the treatment of -10 ㎪. But that of P did not show statistical differences among treatments tested. As the fertigation setting point was getting low, the growth decreased at 60th day after planting, while there were no differences among treatments at 210th day after planting. The fruit quality except sugar contents did not show differences among treatments, but sugar contents was the highest in the treatment of -30 ㎪ with $8.0^{\circ}$Brix. Above results indicated that fertigation setting point should be in the range from -10 ㎪ to -20 ㎪ to ensure good crop growth and fruit quality in sweet pepper production.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.239-253
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• 2024

This research applies both point-wise and pair-wise learning strategies within the learning-to-rank (LTR) framework to predict horse race rankings in Seoul. Specifically, for point-wise learning, we employ a linear model and random forest. In contrast, for pair-wise learning, we utilize tools such as RankNet, and LambdaMART (XGBoost Ranker, LightGBM Ranker, and CatBoost Ranker). Furthermore, to enhance predictions, race records are standardized based on race distance, and we integrate various datasets, including race information, jockey information, horse training records, and trainer information. Our results empirically demonstrate that pair-wise learning approaches that can reflect the order information between items generally outperform point-wise learning approaches. Notably, CatBoost Ranker is the top performer. Through Shapley value analysis, we identified that the important variables for CatBoost Ranker include the performance of a horse, its previous race records, the count of its starting trainings, the total number of starting trainings, and the instances of disease diagnoses for the horse.

• Journal of KIBIM
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• v.9 no.4
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• pp.31-40
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• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.168-178
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• 2005

The Delaunay triangular net is primarily characterized by a balance of the whole by improving divided triangular patches into a regular triangle, which closely resembles an equiangular triangle. A triangular net occurring in certain, point-clustered, data is unique and can always create the same triangular net. Due to such unique characteristics, Delaunay triangulation is used in various fields., such as shape reconstruction, solid modeling and volume rendering. There are many algorithms available for Delaunay triangulation but, efficient sequential algorithms are rare. When these grids involve a set of points whose distribution are not well proportioned, the execution speed becomes slower than in a well-proportioned grid. In order to make up for this weakness, the ids are divided into sub-grids when the sets are integrated inside the grid. A method for finding a mate in an incremental construction algorithm is to first search the area with a higher possibility of forming a regular triangular net, while the existing method is to find a set of points inside the grid that includes the circumscribed sphere, increasing the radius of the circumscribed sphere to a certain extent. Therefore, due to its more efficient searching performance, it takes a shorer time to form a triangular net than general incremental algorithms.