• 한국공작기계학회논문집
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• 제15권6호
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• pp.121-126
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• 2006

Snap-fit is being used in manufacture of plastic products. Integral features using snap-fit are classified as locks, locators and enhancements. Locking features complete the process of attachment by providing physical interference to prevent separation. Looking feature pairs consist of two components, i.e., a flexible latch and a rigid catch and require particular care and attention for their selection. We can make several locking feature pairs by selecting latch and catch, but some parts restrict freedom of selection. Therefore, part designers must know the characteristic properties of generic locking feature forms as considering a specific design problem. In this paper, it has been presented about problem of small size products using locking feature and then introduced new locking feature applicable to small parts.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.192-194
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• 2005

Development of nonlinear loads and compensation instruments make PQ(Power Quality) problem into important issue. Few studies by signal processing and pattern classification as NN(Neural Network), Wavelet Transform, and Fuzzy present feature extraction. A lot of Input features make not always good result and they are difficult to make realtime system. Thus, The dimentionality reduction is indispensable process. PCA(Principal Component Analysis) reduces high-dimensional input features onto a lower-dimensional subspace effectively. It will be useful to apply to realtime system and NN.

• 전자공학회논문지B
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• 제32B권11호
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• 산업경영시스템학회지
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• 제36권3호
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• 수산해양교육연구
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• 제23권4호
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• pp.568-581
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• 2011

This thesis is the study about application development for management obesity and personal health matters systematically based on Android smart phone system. Growing obesity problem for students organized by the lack of a device or program to manage at home has been favored as a serious problem. Currently developed smart phones has come into wide use by portable features and many applications. And to support populations of these features smart phones will be available for obesity management in your home without any equipment using. Accordingly, this paper is the study about application development for management obesity for growing students at home based on the latest smart phone platform Android.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1319-1325
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• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4463-4482
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• 2020

Mesh model generated from 3D reconstruction usually comes with lots of noise, which challenges the performance and robustness of mesh simplification approaches. To overcome this problem, we present a novel method for mesh simplification which could preserve structure and improve the accuracy. Our algorithm considers both the planar structures and linear features. In the preprocessing step, it automatically detects a set of planar structures through an iterative diffusion approach based on Region Seed Growing algorithm; then robust linear features of the mesh model are extracted by exploiting image information and planar structures jointly; finally we simplify the mesh model with plane constraint QEM and linear feature preserving strategies. The proposed method can overcome the known problem that current simplification methods usually degrade the structural characteristics, especially when the decimation is extreme. Our experimental results demonstrate that the proposed method, compared to other simplification algorithms, can effectively improve the quality of mesh and yield an increased robustness on noisy input mesh.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.

• 한국콘텐츠학회논문지
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• 제16권10호
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• pp.617-626
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• 2016

이 연구는 간호학과 학부 과정에서 경험한 시뮬레이션 학습이 신규간호사의 문제해결과정과 임상수행능력에 미친 영향을 파악하기 위한 서술적 조사연구이다. 연구 대상자는 학부과정에서 시뮬레이션 학습 경험이 1회 이상 있으며 임상 경력 12개월 미만의 신규간호사 202명이다. 연구를 통해 학부에서 경험한 시뮬레이션 학습의 횟수와 신규간호사의 문제해결과정 및 임상수행능력에 미친 영향 간에는 유의미한 상관관계가 없었으며, 시뮬레이션 학습의 설계와 문제해결과정 및 임상수행능력에 미친 영향 간에는 유의미한 양의 상관관계가 있는 것으로 나타났다. 연구 대상자들이 경험한 시뮬레이션 학습의 설계를 군집분석으로 유형화하여 문제해결과정 및 임상수행능력에 미친 영향의 차이를 분석한 결과에서도 유의미한 차이를 나타냈다. 이 연구를 통해 학습의 효과를 향상하기 위해서 시뮬레이션 학습의 설계가 중요하다는 점을 확인하였으며, 효과적인 운영을 위한 기초 자료로 활용할 수 있을 것으로 기대한다. 추후 학부 과정의 시뮬레이션 학습이 간호사의 임상 능력에 미치는 영향에 대하여 보다 면밀한 평가가 이루어질 필요가 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.940-943
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• 1996

In teleoperating, as seeing the monitor screen obtained from a camera instituted in the working environment, human operator generally controls the slave arm. Because we can see only 2-D image in a monitor, human operator does not know the depth information and can not work with high accuracy. In this paper, we proposed a traded control method using an visual sensor for the purpose of solving this problem. We can control a teleoperation system with precision when we use the proposed algorithm. Not only a human operator command but also an autonomous visual sensor feedback command is given to a slave arm for the purpose of coincidence current image features and target image features. When the slave arm place in a distant place from the target position, human operator can know very well the difference between the desired image features and the current image features, but calculated visual sensor command have big errors. And when the slave arm is near the target position, the state of affairs is changed conversely. With this visual sensor feedback, human does not need coincide the detail difference between the desired image features and the current image features and proposed method can work with higher accuracy than other method without, sensor feedback. The effectiveness of the proposed control method is verified through series of experiments.