• Korean Journal of Human Ecology
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• v.21 no.5
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• pp.975-988
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• 2012

Although the pattern development for tight-fitting clothing has been carried out using 3D data on humans, the pattern development using 3D scan data obtained for various postures still remains an interesting subject. In this study, we have developed the 2D pattern using the 3D human body reflecting standing and cycling postures. The 3D scan data of a subject was obtained using Cyberware. 2C-AN program(Triangle simplification and the Runge-Kutta method) was used in the system to reduce the 3D scan data points and to make segmented triangular patches in a plane from 3D data. As results, surface distance and area of each body part of standing and cycling postures were also provided for the future application of the functional clothing construction. The area of center piece on the front (c.front) decreased by $106.45cm^2$(-13.08%) and that of lateral piece(s.back) on the back increased by $144.96cm^2$(18.69%) in the patterns of cycling posture. The girth of neck and waist for the cycling posture increased by 0.88cm (3.92%) and 1.56cm(4.40%) respectively, and the that of thigh decreased by 1.01cm(-2.24%). The differences between the area in the 2D pattern obtained from the 3D scan data and that in the 3D scan surface data for standing and cycling postures were very small($-10.34cm^2$(-0.32%) and $-44.33cm^2$(-1.32%)).

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1491-1499
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• 2000

This paper is designed and implemented the data recovery mechanism for 2-D (2-dimensional) bar code system. The data recovery algorithm used the modified Reed-Solomon algorithm and it is implemented into 2-D bar code system. There are 7 types of 2-D bar code system, which are 21x21, 25x25, 41x41, 73x73, 101x101, 177x177. This paper has been experimented that how many data is saved among several 2-D bar code types and how many data re recovered. In the first experiment, the big size 2-D bar code system has many ECC codeword. Therefore, original data cannot be assigned to 2-D bar code system. In the second experiment, even if 35∼40% loss dta for the 2-D bar code system, the 2-D bar code system could have been recovered to original data.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.179-187
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• 2005

The usage of mixed 2D and 3D CAD data of commercial CAD systems is required in the construction practice. Sometimes 3D wireframe model is required by end-users when 2D CAD data is delivered. However, current KOSDIC can not represent 3D CAD data, because it has been developed as a 2D drawing delivery standard. Therefore, this study is to provide exchange and sharing of mixed 2D and 3D CAD data that add 3D wireframe model in the KOSDIC. To achieve this purpose, the authors have investigated the 3D CAD entities of commercial CAD systems, and have analyzed STEP standards providing 3D wireframe model. The result, the authors have extracted 3D CAD common entities based wireframe model which shall be added in the KOSDIC. This study can be beneficial by using the developed data model for heterogeneous CAD systems, and by providing the representation of mixed 2D and 3D CAD data in construction practice such as GIS, piping system, and so forth.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.208-217
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• 2021

3D-CNN is one of the deep learning techniques for learning time series data. However, these three-dimensional learning can generate many parameters, requiring high performance or having a significant impact on learning speed. We will use these 3D-CNNs to learn hand gesture and find the parameters that showed the highest accuracy, and then analyze how the accuracy of 3D-CNN varies through input data changes without any structural changes in 3D-CNN. First, choose the interval of the input data. This adjusts the ratio of the stop interval to the gesture interval. Secondly, the corresponding interframe mean value is obtained by measuring and normalizing the similarity of images through interclass 2D cross correlation analysis. This experiment demonstrates that changes in input data affect learning accuracy without structural changes in 3D-CNN. In this paper, we proposed two methods for changing input data. Experimental results show that input data can affect the accuracy of the model.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.268-274
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• 2007

Traditional two-dimensional (2D) interpretation of magnetotelluric (MT) data utilizes only transverse magnetic (TM)-mode data, because 2D inversion of transverse electric (TE)-mode data results in spurious features when 3D structures exist in the subsurface. The application of a 3D inversion algorithm to a single MT profile can reduce contamination due to off-profile anomalies and help us to incorporate TE-mode data in the interpretation. In this study, we conduct 2D and 3D inversions of MT data observed along two lines in Jeju Island. First, we invert apparent resistivities and phases in the TM and TE modes separately. Then, we perform 2D joint inversion of both TM- and TE-mode data and 3D inversion of both Zxy- and Zyx-mode data corresponding to TE- and TM-mode data in 2D. The resistivity images derived from all four data show that the geoelectrical structure in Jeju Island is a three-layered earth with the resistive-conductive-resistive stratigraphy within a depth of 5 km. The 3D inversion does not produce clear anomalies in the reconstructed profile image, while all of 2D do. This attributed to the possibility that 2D inversion results are distorted by exiting off-profile 3D anomalies in Jeju. With 3D inversion of 2D profile MT data, we can deduce more reliable results that are not seriously distorted by off-profile 3D anomalies.

• Korean Journal of Human Ecology
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• v.19 no.3
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• pp.555-563
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• 2010

With recent advances in 3D scanning technology, three-dimensional (3D) patternmaking is becoming a powerful way to develop garments pattern. This technology is now applicable to the made to measure (MTM) system of both ordinary and tightly fitting garments. Although the pattern of fitted clothing has been developed using 3D human data, it is still interesting to develop cycling pants by considering while-cycling body posture and fabric elasticity. This study adopted the Garland's triangle simplification method in order to simplify data without distorting the original 3D scan. Next, the Runge-Kutta method (2C-AN program) was used to develop a 2D pattern from the triangular pixels in the 3D scanned data. The 3D scanned data of four male, university students aged from 21 to 25, was obtained using Whole body scanner (Model WB4, Cyberware, Inc., USA). Results showed the average error of measurement was $4.58cm^2$ (0.19%) for area and 0~0.61cm for the length between the 3D body scanned data and the 2D developed pattern data. This is an acceptable range of error for garment manufacture. Additionally, the 2D pattern developed, based on the 3D body scanned data, did not need ease for comfort or ease of movement when cycling. This study thus provides insights into how garment patterns may be developed for ergonomic comfort in certain special environments.

• Korean Journal of Computational Design and Engineering
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• v.11 no.6
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• pp.447-454
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• 2006

Although there are many needs to use 3D models in MEMS field, it is not easy to generate 3D models based on MEMS CAD. This is because MEMS CAD is based on 2D and their popular format-GDSII file format- has its own limits and problems. The differences between GDSII file format and 3D CAD system, such as (1) superposed modeling, (2) duplicated entity, (3) restricted of entity type, give rise to several problems in data exchange. These limits and problems in GDSII file format have prevented 3D CAD system from generating 3D models from the MEMS CAD. To remove these limits and solve problems, it is important to extract the silhouette of data in the MEMS CAD. The proposed method has two main processes to extract silhouette; one is to extract the pseudo-silhouette from the original 2D MEMS data and the other is to remove useless objects to complete the silhouette. The paper reports on the experience gained in data exchange between 2D MEMS data and 3D models by the proposed method and a case study is presented, which employs the proposed method using MEMS CAD IntelliMask and Solidworks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.815-820
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• 2012

We introduce two-dimensional soft output Viterbi algorithm (2D SOVA) and iterative 2D SOVA for holographic data storage. Since the holographic data storage is 2D intersymbol interference (ISI) channel, the 2D detection schemes have good performance at holographic data storage. The 2D SOVA and iterative 2D SOVA are 2D detection schemes. We introduce and compare the two 2D detection schemes. The 2D SOVA is approximately 2 dB better than one-dimensional (1D) detection scheme, and iterative 2D SOVA is approximately 1 dB better than the 2D SOVA. In contrast, the iterative 2D SOVA is approximately twice complex higher than 2D SOVA, and 2D SOVA is approximately twice complex higher than 1D detection scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.113-118
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• 2010

Recently, 3D-reconstruction for geographic information and study of geospatial information is progressing in various fields through national policy such as R&D business and pilot project. LiDAR system has a advantage of acquisition the 3D information data easily and densely so that is used in many different fields. Considering to characterist of the point data formed with 3D, it need a high specification CPU because it requires a number of processing operation for 2D form expressed by monitor. In contrast, 2D grid structure, like DEM, has a advantage on costs because of simple structure and processing speed. Therefore, purpose of this study is to solve the problem of requirement of more storage space, when LiDAR data stored in forms of 3D is used for 3D-geographic and 3D-buliding representation. Additionally, This study reconstitutes 2D-gird data to supply the representation data of 3D-geographic and presents the storage method which is available for detailed representation applying tree-structure and reduces the storage space.