• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.546-548
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• 1998

This paper deals with ZMP trajectory generation in multi step walking of IWR-III(Inha Walking Robot) Biped Walking Robot. Biped walking is realized by combining 6-types of pre-defined steps and the actual ZMP can be used as a stability index of a robot. For the good tracking of actual ZMP, desired ZMP trajectory is generated during the whole walking time not for each step. Trajectory generation is performed considering velocities and accelerations of given via points using 5-th order polynomial interpolation method. As a result, balancing joints have a more smooth and continuous motion and actual ZMP has a better tracking ability. Numerical simulator is done by MATLAB to guarantee the walking of a robot satisfying the ZMP stability.

• Bulletin of the Korean Chemical Society
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• 제25권6호
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• pp.895-899
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• 2004

We have designed a new family of radioligands, 3-(amino- and hydroxymethyl)-4-(5-iodopyridin-2-ylthio)indoles, combining characteristically distinct moieties proven to impart successful binding ability in a variety of structurally diverse selective serotonin reuptake inhibitors recently published. Described in this article are the syntheses of 3-substituted 4-(5-iodopyridin-2-ylthio)-indoles, featuring successful adaptation of the modified Leimgruber-Batcho indole synthesis onto the key intermediate 1-(5-iodopyridin-2-ylthio)-2-methyl-3-nitrobenzene (6) prepared from the nucleophilic aromatic substitution of chloropyridine 7 with thiophenol 8.

• Structural Engineering and Mechanics
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• 제12권3호
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• pp.231-248
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• 2001

The three-dimensional behavior of confined concrete was investigated, including strength enhancement due to triaxial compressive stresses, lateral expansion, compression softening, cover spalling and post-peak ductility. A finite element program based on a nonlinear elasticity methodology was employed to evaluate the ability to model triaxial behavior of reinforced concrete (RC) by combining constitutive models proposed by several researchers. The capability of compression field based models to reproduce the softening behavior of lightly cracked confined concrete was also investigated. Data from tested specimens were used to evaluate the validity of the formulations. Good agreement with the experimental results was obtained.

• 아동학회지
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• 제25권6호
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• pp.259-277
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• 2004

The effects of two literacy programs, Balanced Literacy Instruction(BLI) and Phonics Instruction were studied in 40 three- and four-year old children. BLIwas created to compensate for the limits of the Whole Language Approach(WLA) and of Phonics Instruction(PI). The WLA focuses on writing and communication but lacks concrete instruction methods and overlooks technical aspects of reading and writing. On the contrary, PI is logical, stressing phonemic recognition, skill in distinguishing words, and the rules of consonants and vowels. By combining the best of both PI and WLA to create BLI, this study showed that children who received BLI significantly improved their ability of phoneme recognition, reading, writing and understanding of the content of a book, letting us conclude that BLI is the more effective method for teaching literacy.

• 대한치과의사협회지
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• 제52권8호
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• pp.478-490
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• 2014

Recently, importance of meta-analysis is increasing in the field of dentistry, since it is not easy to settle controversies arising from conflicting studies. Meta-analysis is the statistical method of combining results from two or more individual studies that have been done on the same topic. Merits of meta-analysis include an increase in power, an improvement in precision, and the ability to address solution not provided by individual studies. However, it might mislead researchers when variation across studies and publication bias are not carefully taken into consideration. The purpose of this study is to help understand meta-analysis by making use of individual results in dental research paper.

• 전기학회논문지
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• 제60권2호
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• pp.423-430
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• 2011

In adaptive filtering, the sparseness of impulse response and input signal characteristics are very important factors of it's performance. This paper presents a subband improved proportionate normalized least square (SIPNLMS) algorithm which combines IPNLMS for impulse response sparseness and subband filtering for prewhitening the input signal. As drawing and combining the advantage of conventional approaches, the proposed algorithm, for impulse responses exhibiting high sparseness, achieve improved convergence speed and tracking ability. Simulation results, using colored signal(AR(4)) and speech input signals, show improved performance compared to fullband structure of existing methods.

• 공학교육연구
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• 제22권2호
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• pp.36-45
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• 2019

Capstone design is a subject taught in a setting where students gather in a team, decide on their own selected topic, and collaborate with one another to perform a project. A fair assessment is very important in a team project-based capstone design course for students. Many instructors agree that harmonizing creative evaluation and outcome assessment is hard in capstone design class. In also, it is not easy to assess students' individual efforts and achievements fairly in accordance with team-based assessment practices. To resolve this issue in this paper, we have surveyed various engineering design education methodologies, and have modelled existing evaluating elements into a modified creative process and outcome assessment framework for team project assessment. In particular, we focused on a method of fairly assigning credits by combining team based and individual-level assessments. Analyzing students' achievement and grade evaluation and verifying the validity of the proposed method was performed.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.111-120
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• 2020

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

• 로봇학회논문지
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• 제17권4호
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• pp.425-430
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• 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.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.133-138
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• 2024

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.