• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.2
• /
• pp.168-177
• /
• 2008

Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.

• Computational Structural Engineering
• /
• v.7 no.4
• /
• pp.85-101
• /
• 1994

A family of variational principles governing the dynamics of laminated plate has been derived using a variationally consistent shear deformable discrete laminated plate theory with particular reference to finite element procedures. The theoretical basis for the derivation is Sandhu's generalized procedure for the variational formulation of linear coupled boundary value problem. As the bilinear mapping to write the operator matrix of the field equations in self-adjoint form, convolution product was employed. Boundary conditions, initial conditions and probable internal discontinuity were explicitly included in the governing functionals. Some interesting extensions and specializations of the general variational principle were presented, which can provide many different finite element formulations for the problem.

• Publications of The Korean Astronomical Society
• /
• v.30 no.2
• /
• pp.75-78
• /
• 2015

A growing body of evidence has been supporting the existence of so-called "dark molecular gas" (DMG), which is invisible in the most common tracer of molecular gas, i.e., CO rotational emission. DMG is believed to be the main gas component of the intermediate extinction region from Av~0.05-2, roughly corresponding to the self-shielding threshold of $H_2$ and $^{13}CO$. To quantify DMG relative to $H{\small{I}}$ and CO, we are pursuing three observational techniques; $H{\small{I}}$ self-absorption, OH absorption, and THz $C^+$ emission. In this paper, we focus on preliminary results from a CO and OH absorption survey of DMG candidates. Our analysis shows that the OH excitation temperature is close to that of the Galactic continuum background and that OH is a good DMG tracer co-existing with molecular hydrogen in regions without CO. Through systematic "absorption mapping" by the Square Kilometer Array (SKA) and ALMA, we will have unprecedented, comprehensive knowledge of the ISM components including DMG in terms of their temperature and density, which will impact our understanding of galaxy evolution and star formation profoundly.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2762-2764
• /
• 2001

In this study, we introduce and investigate a class of dynamic perceptron architectures, discuss a comprehensive design methodology and carry out a series of numeric experiments. The proposed dynamic perceptron architectures are called as Polynomial Neural Networks(PNN). PNN is a flexible neural architecture whose topology is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated on the fly. In this sense, PNN is a self-organizing network. PNN has two kinds of networks, Polynomial Neuron(FPN)-based and Fuzzy Polynomial Neuron(FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based Self-organizing Polynomial Neural Networks(SOPNN) dwells on the Group Method of Data Handling (GMDH) [1]. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neurofuzzy systems. A detailed comparative analysis is included as well.

• Journal of The Korean Astronomical Society
• /
• v.33 no.1
• /
• pp.1-17
• /
• 2000

We have performed extensive simulations of response of gaseous disk in barred galaxies using SPH method. The gravitational potential is assumed to be generated by disk, bulge, halo, and bar. The mass of gaseous disk in SPH simulation is assumed to be negligible compared to the stellar and dark mass component, and the gravitational potential generated by other components is fixed in time. The self-gravity of the gas is not considered in most simulations, but we have made a small set of simulations including the self-gravity of the gas. Non-circular component of velocity generated by the rotating, non-axisymmetric potential causes many interesting features. In most cases, there is a strong tendency of concentration of gas toward the central parts of the galaxy. The morphology of the gas becomes quite complex, but the general behavior can be understood in terms of simple linear approximations: the locations and number of Lindblad resonances play critical role in determining the general distribution of the gas. We present our results in the form of 'atlas' of artificial galaxies. We also make a brief comment on the observational implications of our calculations. Since the gaseous component show interesting features while the stellar component behaves more smoothly, high resolution mapping using molecular emission line for barred galaxies would be desirable.

• International Journal of Contents
• /
• v.17 no.4
• /
• pp.1-15
• /
• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.323-324
• /
• 2022

본 논문에서는 사람의 개입 없이 실내 환경에서 마스크 미 착용자를 스스로 발견한 후 방역수칙위반 사실에 대한 경고와 함께 마스크 착용을 권고하는 인공지능 기반의 자율주행 서비스 로봇을 개발한다. 제안한 시스템에서 로봇은 동시적 위치 추적 지도 작성 기법인 SLAM(Simultaneous Localization and Mapping)기술을 이용하여 지도를 작성한 후 사용자가 제공한 웨이포인트(Waypoint)를 기반으로 자율주행한다. 또한, YOLO(You Only Look Once) 알고리즘을 이용한 실시간 객체 인식 기술을 활용하여 보행자의 마스크 착용 여부를 판단한다. 실험을 통해 사전에 작성된 지도에 지정된 웨이포인트를 따라 로봇이 자율주행하는 것을 확인하였다. 또한, 충전소로 이동할 경우, 영상 처리 기법을 활용하여 충전소에 부착된 표식에 근접하도록 이동하여 충전이 진행됨을 확인하였다.

• Journal of The Korean Association For Science Education
• /
• v.38 no.1
• /
• pp.43-55
• /
• 2018

In this study, we investigated high school students' analogy generating processes using the think-aloud method. Twelve high school students in Seoul participated in this study. The students were asked to generate analogies on ionic bonding and were also interviewed after their activities. Their activities and interviews were recorded and videotaped. After classifying the analogy generating processes into the three stages-encoding, exploring sources, and mapping, several process components were identified. The analyses of the results indicated that they checked the target concept given and selected one for a salient attribute among many attributes of the target concept at the stage of encoding. After selecting the salient attribute, they translated the salient attribute that is a scientific term into an everyday term, which is named as 'extracting salient similarities.' At the stage of exploring sources, they chose the sources based on salient similarities and chose the final source through circular processes, which included the process components of 'evaluating the sources' and 'discarding the sources.' At the final stage, they added the attributes to analogs and mapping them to the attributes of the target concept, which is named as 'mapping shared attributes.' There were some cases that 'mapping shared attributes' appeared after they specified the situation of analogs or assumed new situation, which is named as 'specifying the situations.' Some students recognized unshared attributes in their analogs.

• Journal of The Korean Association For Science Education
• /
• v.37 no.3
• /
• pp.407-416
• /
• 2017

In this study, we investigated the processes of analogy change and the characteristics of discussions in collaborative activity of self-generated analogy. Twenty-four high school students in Seoul participated in this study. We implemented science instructions based on collaborative activity of self-generated analogy. We compared personal analogies, group analogies, and modified group analogies in order to analyze the processes of analogy change. We also analyzed the characteristics of group and classroom discussions in the science instructions. The analyses of the results indicated that the processes of analogy change were categorized into three patterns; adding shared attributes, recognizing unshared attributes, and revising mapping errors. They selected a group analogy from analogies of their group members by considering inclusiveness, originality, and familiarity. They perceived the activity of self-generated analogy as subjective and creative. Therefore, they felt little pressure of self-generated analogy and there were little conflicts in group discussions. On the other hand, various analogies were suggested in classroom discussions and the competitive atmosphere of classroom discussions led students to focus on unshared attributes. At the stage of modifying group analogies, they added unshared attributes as limitations of the group analogy and changed their group analogy not to have unshared attributes. There were no cases of generating a new analogy. Some suggestions to implement collaborative activity of self-generated analogy in science teaching effectively are discussed.

• Advances in concrete construction
• /
• v.9 no.1
• /
• pp.55-70
• /
• 2020

In this research article, acid resistance, sulphate resistance and sorptivity of self compacted concrete (SCC) prepared from C&D waste have been discussed. To improve the above properties of self compacted recycled aggregate concrete (SCRAC) along with mechanical and durability properties, different two stage mixing approaches (TSMA and TSMAsfc) were followed. In the proposed two stage mixing approach (TSMAsfc), silica fume, a proportional amount of cement and a proportional amount of water were mixed in premix stage which fills the pores and cracks of recycled aggregate concrete (RAC). The concrete specimen prepared using above mixing approaches were immersed in 1% concentration of sulphuric acid (H2SO4) and magnesium sulphate (MgSO4) solution for 28, 90 and 180 days for evaluating the acid resistance of SCRAC. Experimental results concluded that the proposed two stage mixing approach (TSMAsfc) is most suitable for acid resistance and sulphate resistance in terms of weight loss and strength loss due to the elimination of pores and cracks in the interfacial transition zone (ITZ). In modified two stage mixing approach, the pores and cracks of recycled concrete aggregate (RCA) were filled up and make ITZs of SCRAC stronger. Microstructure analysis was carried out to justify the reason of improvement of ITZs by electron probe micro analyser (EPMA) analysis. X-ray mapping was also done to know the presence of strength contributing elements presents in the concrete sample. It was established that SCRAC with modified mixing approach have shown improved results in terms of acid resistance, sulphate resistance, sorptivity and mechanical properties.