• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.5
• /
• pp.119-126
• /
• 2000

Recently, according to the various taste of consumers, the design of a product is changed variously and complicatedly. The complicated product is not usually constructed with one path but multi-path. By the way, in machining, higher precision and the reduction of leading and machining time is required. But, for the multi-patch sculptured surface, the amount of machining data becomes large. This means the increase of leading and machining time. In this study, the tool path generation method with reparameterization is proposed for multi-patch sculptured surface and variable step size using NURBS is used to satisfy the precision and to reduce leading and machining time.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.04a
• /
• pp.647-652
• /
• 2000

Recently, according to the various taste of consumers, the design of a product is changed variously and complicatedly. The complicated product is not usually constructed with one patch but multi-patch. By the way, in machining, higher precision and the reduction of leading and machining time is required. But for the multi-patch sculptured surface, the amount of machining data becomes large. This means the increase of leading and machining time. In this study, the tool path generation method with reparameterization is proposed for multi-patch sculptured surface and variable step size using NURBS is used to satisfy the precision and to reduce leading and machining time.

• Journal of the Korean Data and Information Science Society
• /
• v.9 no.2
• /
• pp.119-138
• /
• 1998

The case of proportional cell frequencies for the two-way cross-classification with interaction is considered. Several types of hypotheses for the general unbalanced data that are commonly used in the literature are shown, and they are written out for this particular case. A reparameterized form of the cell means model is defined to establish the reparameterized model, and orthogonal property of the model is shown using the augmented matrix and the numerator sums of squares are computed. Different ways of producing the same analysis of variance tables are shown in both orthogonal and nonorthogonal situations.

• Communications for Statistical Applications and Methods
• /
• v.23 no.2
• /
• pp.179-187
• /
• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2341-2344
• /
• 2001

In recent years the neural network known as a sort of the intelligent control strategy is used as a powerful tool for designing control system since it has learning ability. But it is difficult for neural network controllers to guarantee the stability of control systems. In this paper we try connecting a radial basis function network to an adaptive control strategy. Radial basis function networks are simpler and easier to handle than multilayer perceptrons. We use the radial basis function network to generate control input signals that are similar to the control inputs of adaptive control using linear reparameterization of the robot manipulator. We adopt the saturation function as an auxiliary controller. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.836-842
• /
• 2006

3D 애니메이션 제작에서 동작의 타이밍(예를 들면 timing&spacing, slow-in, slow-out)은 연기의 의미와 느낌을 정확히 표현하기 위한 매우 중요한 요소 중의 하나이다. 따라서 이러한 타이밍의 편집은 애니메이션 작업에서 필수적이라고 할 수 있는데, 이를 기존의 3D 애니메이션 시스템에서 수행하기에는 기술적으로 많은 어려움이 있었다. 첫째로 타이밍의 편집은 시간축 자체를 변형하는 문제이기 때문에 보간 곡선에 대한 재매개변수화가 필요한데, 이러한 가능은 기존 애니메이션 시스템에서 제공되지 않는다. 둘째로 타이밍 편집에는 종종 애니메이션 감독이 직접 참여하기도 하는데, 일반적으로 3D 애니메이션 시스템의 사용에 익숙하지 않기 때문에 원하는 결과를 직접 만들어 보기가 어려웠다. 본 논문에서는 이러한 문제들을 해결한 새로운 애니메이션 타이밍 시스템을 구현하였다. 이 시스템은 렌더링된 영상파일들과 애니메이션 장면 파일을 입력 받아 사용자가 타이밍 편집을 하고, 그 결과를 애니메이션 장면 파일에 다시 기록하는 방식으로 구현된다. 타이밍 편집은 기존 셀 애니메이션 제작 방식과 유사한 방식으로 프레임을 삽입하거나 삭제하는 가능과 시간왜곡 (time-warping) 그래프를 직접 조정하여 타이밍을 조정하는 가능을 제공한다. 전자는 제작도구에 익숙하지 않은 감독이나 셀 애니메이션 작업자들이 직관적으로 사용할 수 있는 기능이고, 후자는 좀 더 세밀한 타이밍 조정을 위해 제공하는 가능이다. 사용자가 편집한 타이밍 결과는 각 동작변수의 보간곡선을 재매개변수화하여 애니메이션 파일에 기록된다. 본 논문에서 구현한 시스템은 실제 애니메이션 제작에 보편적으로 사용되는 마야 애니메이션 파일을 지원하도록 구현되었다.

• Journal of IKEEE
• /
• v.26 no.2
• /
• pp.160-168
• /
• 2022

In this paper, we propose a 3D mesh reconstruction method from a single image using deep learning and a sphere shape transformation method. The proposed method has the following originality that is different from the existing method. First, the position of the vertex of the sphere is modified to be very similar to the 3D point cloud of an object through a deep learning network, unlike the existing method of building edges or faces by connecting nearby points. Because 3D point cloud is used, less memory is required and faster operation is possible because only addition operation is performed between offset value at the vertices of the sphere. Second, the 3D mesh is reconstructed by covering the surface information of the sphere on the modified vertices. Even when the distance between the points of the 3D point cloud created by correcting the position of the vertices of the sphere is not constant, it already has the face information of the sphere called face information of the sphere, which indicates whether the points are connected or not, thereby preventing simplification or loss of expression. can do. In order to evaluate the objective reliability of the proposed method, the experiment was conducted in the same way as in the comparative papers using the ShapeNet dataset, which is an open standard dataset. As a result, the IoU value of the method proposed in this paper was 0.581, and the chamfer distance value was It was calculated as 0.212. The higher the IoU value and the lower the chamfer distance value, the better the results. Therefore, the efficiency of the 3D mesh reconstruction was demonstrated compared to the methods published in other papers.

• Geophysics and Geophysical Exploration
• /
• v.25 no.4
• /
• pp.227-241
• /
• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.408-415
• /
• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.