• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.5
• /
• pp.83-92
• /
• 2011

IDEF is a widely used methodology for traditional structured software development. As object-oriented softwares are widely used, an object-oriented version of IDEF, IDEF4, is developed. UML is de facto standard for object-oriented software development methods. Whereas IDEF is widely used for CALS/EC, UML is used for general object-oriented software development. Most software developers are not familiar with IDEF4 but familiar with UML. Moreover, UML has many CASE tools. So we can develop software efficiently if we convert IDEF4 model to UML model. In this paper, we transform IDEF4 models to UML models. We explain the rules and methods to convert IDEF4 model to UML model and applied the transform methods and rules to a case study.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.6
• /
• pp.123-130
• /
• 2007

In this paper, We define convert rules from objects and relation presented in object model to the state and operation domain in formal specification. Namely, object and relation in information model converted to state domain in formal specification. State, event and behavior converted to operation domain. And that way informal object model change to formal language, it can be verify through formal method. Verification process make an offer convenience and confidence in software development early phase. And we implement simulation tool in order to verification method of formal specification and to consistency verified model between user's requirement. It is possible to select the suitable model and reduce the costs and efforts on software development.

• The Journal of Korea Robotics Society
• /
• v.4 no.3
• /
• pp.185-191
• /
• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.27 no.6
• /
• pp.557-564
• /
• 2014

In this study, we present an algorithm that conducts an unique naming process for the bridge object through the solid object identification focused on 3D straight bridge model. For the recognition of 3D objects, the numerical algorithm utilizes centroid point, and solid object on the local coordination system. It classifies the object feature set by classifying the objects and members based on the bridge direction. By doing so, unique names, which contain the information about span, members and order of the object, were determined and the suitability of this naming algorithm was examined through a truss bridge model and a bridge model with different coordinate systems. Also, the naming process based on the object feature set was carried out for the real 3D bridge model and then was applied to the module on local server and mobile device for real bridge inspection work. From the comparison of the developed naming algorithm based on object identification and the conventional one based on field inspection, it was shown that the conventional field inspection work can be effectively improved.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.1
• /
• pp.449-459
• /
• 2014

The proposed 2+1 view integrated metamodel defined formerly with a graphical class model can not be guaranteed the syntactic clarity and accuracy precisely for the metamodel due to the informal specification. This paper specifies the syntactic semantics formally for the 2+1 view integrated metamodel using Z and Object-Z and checks the accuracy of the metamodel with Z/Eves tool. The formal specification is expressed in Z and Object-Z schema separately for syntax and statics semantics of the 2+1 view integrated metamodel, which applying the converting rule between class model and Z/Object-Z. The accuracy of the Z specification for the metamodel is verified using Z/Eves tool, which can check the syntax, type, and domain of the Z specification. The transformation specification and checking of the 2+1 view integrated metamodel can help establish more accurate the syntactic semantics of its construct and check the accuracy of the metamodel.

• The Journal of Korea Robotics Society
• /
• v.15 no.1
• /
• pp.8-15
• /
• 2020

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re³). Re³ has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

• The Journal of Korea Robotics Society
• /
• v.17 no.4
• /
• pp.425-430
• /
• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.210-214
• /
• 2008

It is actually impossible to model and store all objects which exist in real home environment into robot's database in advance. To resolve this problem, this paper proposes new object modeling method that can be available for robot self-modeling, which is capable of estimating whole model's shape from partial surface data using Generic Model. And this whole produce is conducted to cylindrical objects like cup, bottles and cans which can be easily found at indoor environment. The detailed process is firstly we obtain cylinder's initial principle axis using points coordinates and normal vectors from object's surface after we separate cylindrical object from 3D image. This 3D image is obtained from 3D sensor. And second, we compensate errors in the principle axis repeatedly. Then finally, we do modeling whole cylindrical object using cross sectional principal axis and its radius To show the feasibility of the algorithm, We implemented it and evaluated its accuracy.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.2
• /
• pp.69-76
• /
• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• The Journal of the Korea Contents Association
• /
• v.10 no.12
• /
• pp.18-26
• /
• 2010

In this paper, we propose a method for generating a realistic object panorama by considering geometric transformations of camera views in a general photographing environment. In the proposed method, we first model a trumpet-shape panorama space based on geometric transformations of camera, such as vertical rotation and horizontal rotation movement around a target model. We then generate an object panorama by mapping model images to the trumpet-shape panorama space. To evaluate the performance of the proposed method, experiments were conducted on a large size model, which is quite difficult for us to generate the object panorama without special equipments in general. The experimental results show that the proposed method can effectively generate an object panorama, which is usually generated in a special photographing environment, regardless of the size of target model.