• International conference on construction engineering and project management
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• 2005.10a
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• pp.791-795
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• 2005

A simulation model is proposed to evaluate the effect of correlations between activity durations on the overall project duration. The proposed model incorporates NORTA, a recent developed statistical method, into the simulation process to allow arbitrarily specified marginal distributions for activity durations and any desired correlation structure. The generality is of practical value when systematic data is not available and planners have to rely on arbitrary experts' estimation, which may involve a mixed situation when some activity durations are continuously distributed whereas others are discrete outcomes. The proposed model is validated by showing that the correlation coefficients of the simulation results are close to the originally specified ones. The simulation results are compared to two conventional approaches: PERT and simulation without correlation. The comparisons illustrate that the proposed model can provide important management information, which would otherwise be distorted due to the neglect of the correlations between activity durations.

• Journal of information and communication convergence engineering
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• v.21 no.2
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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.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.565-575
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• 2017

This study analyzes the epistemic considerations and the argumentation level revealed in the discourse of the key concept of natural selection for science-gifted elementary students. The paper analyzes and discusses the results of a three-student focus group, drawn from a cohort of twenty gifted sixth-grade elementary students. Nature, generality, justification, and audience were used to analyze epistemic consideration. Learning progression in scientific argumentation including argument construction and critique was used to analyze students' scientific argumentation level. The findings are as follows: First, Epistemic considerations in discourse varied between key concepts of natural selection discussed. The nature aspect of epistemic considerations is highly expressed in the discourse for all natural selection key concepts. But the level of generality, justification and audience was high or low, and the level was not revealed in the discourse. In the heredity of variation, which is highly expressed in terms of generality of knowledge, the linkage with various phenomena against the acquired character generated a variety of ideas. These ideas were used to facilitate engagement in argumentation, so that all three students showed the level of argumentation of suggestions of counter-critique. Second, students tried to explain the process of speciation by using concepts that were high in practical epistemic considerations level when explaining the concept of speciation, which is the final natural selection key concept. Conversely, the concept of low level of epistemic considerations was not included as an explanation factor. The results of this study suggest that students need to analyze specific factors to understand why epistemological decisions are made by students and how epistemological resources are used according to context through various epistemological resources. Analysis of various factors influencing epistemological decisions can be a mediator of the instructor who can improve the quality and level of the argumentation.

• Journal of Science of Art and Design
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• v.1
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• pp.189-226
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• 1999

The purpose of this paper is to examine the common characteristics of late 20th century interior design style and the post-modern thinking system. A period has its own predominant spirit which determine the tendency of art, and the ubiquitous power in the spirit decides the period's pattern of art. Therefore the interior design style belongs to general art sphere that has its own individual character derived from the dominant power of the controlling system. Paying special attention to this understanding, the auther has processed this paper by combining the post-modem thinking with special characteristics of each of the 20th century interior design style. Until now, researches have been focused on the individual style of post-modern design; however, a general research comprising the whole characteristics of expression has not been made. Accordingly, the rationale of emphasizing the general point of view is establshed. Also, this study suggests a model applicable to studies concerning other art area. This type of methodology is receving more attention as an approach investigating new art ideology for researching post-modern thinking and late 20th century art styles. The conclusions are as follows: First, the distinctive expressions of postmodernism appear to be characteristic of illusion, metaphor, pluralism, decoration, and symmetry, Those of late modernism appear to be the characteristic of complicated simplicity and symplified complexity, passiveness of symbolism and harmony, indeterminacy of form and space, and unintentional decorativeness. And the characteristics of deconstructionism are spatial difference and temporal defferal, and un-reductive and non formal abstractiveness of the space, unfinishness, chance, and secretiveness of individual style. Second, the disinctive expression seems to share common characteristics with postmodern thinking. The best examples are pluralism, non-formalism, populism, and historicism, originating from the deconstruction of 'meta-narrative'. Third, based on the second conclusion, general distinctive expression could be simplified as chacteristics of plurality, hybridity, and indeterminacy. These expressive chacteristics appear to be automatically connected with general postmodern thinking. Last, in consideration of the above conclusion, the extreme generality could be distinctively clarified as 'textural co-exsistence'. Accordingly, the author might confirm that 'textural co-existence' originated from the text that comes from postmodern thinking. In conclusion, design expression of late 20th century interior design accepts the ?universal theme of ubiquitous postmodern thinking. And universal expressions and supreme generality can be common analysis tools for understanding and studying complicated late 20th century interior design.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.1-24
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• 2016

This paper is to consider trends and issues of regional research in major sub-fields of human geography in Korea, following the previous one which dealt with contexts and general trends of new regional geography in Korea since the 2000s. They include historical and cultural geography on place and landscape, economic geography on industrial districts or agglomerated regions (i.e. clusters) and urban (and social) geography on urban networks and differentiation. Even though researchers in sub-fields have used different terms and concepts to identify region, they are in common to relate specificities of region to general processes such as (de)modernization, (de)industrialization, and globalization, to understand region as social and discursive constitution as well as substantive reality, and to give more attention to socio-spatial networks and relationality than territoriality of regions. These common points seem to reflect the emerging trend of new regional geography, and to get rid of existing traditional concept of region. It is suggested that major tasks for conceptualization of region in future research are to overcome dichotomy of speciality and generality, of substantive reality and discursive constitution, and of territoriality and relationality, and that important issues for empirical research on region include regional synthesis from new perspectives, uneven regional development as relational process in and between regions, and producing practice for alternative regions.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.25-38
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• 2023

Among the discussions on affective representation, the first is to explain the affective representation in the dimensions, and the second is to explain the affective representation according to the modality. In previous studies, to explain affective representation, valence models (signed valence, unsigned valence) and Modality-generality models (modality-general, modality-specific) were presented. In this study, we compared models presented in the previous study using the recently published ASMR to confirm which models explain affective representation well. The data used in this study were behavioral rating values collected by Kim & Kim (2022), and these were obtained for ASMR stimuli that were divided into three affective types (negative, neutral, and positive) and two modalities (auditory and audiovisual). Then, a multidimensional scaling, a representational similarity analysis with a two-way repeated measures ANOVA, and a multiple regression analysis with a two-way repeated measures ANOVA were performed. The results revealed that signed valence and modality-general distinguished between affective types of stimuli better than unsigned valence and modality-specific. Similar to the results of multidimensional scaling, the results of a representational similarity analysis and a multiple regression also showed that the signed valence and modality-general significantly explained affective representation better than the unsigned valence and the modality-specific. These results suggest that the model in which positive and negative are located at the opposite ends of the one dimension explains the affective representation of ASMR well, and that the affective representation was consistent regardless of modality.

• Journal of the Korea Safety Management & Science
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• v.15 no.3
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• pp.133-141
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• 2013

As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author's experiences, consultation with experts, and literature reviews.

• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.387-392
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• 2012

A series of Pd-based catalysts were prepared and examined for the amination of 2,6-dimethylphenol in a fixedbed reactor. The best results were obtained for Pd/$Al_2O_3$-BaO with a conversion of 99.89% and a selectivity of 91.16%. These catalysts were characterized using BET, XRD, XPS, TEM and $NH_3$-TPD. Doped BaO not only improved the dispersion of the Pd particles but also decreased the acidity of the catalyst, which remarkably enhanced the selectivity and stability of the catalyst. The generality of Pd/$Al_2O_3$-BaO for this kind of reaction was demonstrated by catalytic aminations of o- and p-alkyl phenols.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.577-583
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• 2013

Recently, massive archives of ground information imagery from new sensors have become available. To establish a functional relationship between the image and the ground space, sensor models are required. The rational functional model (RFM), which is used as an alternative to the rigorous sensor model, is an attractive option owing to its generality and simplicity. To determine the rational polynomial coefficients (RPC) in RFM, however, we encounter the problem of obtaining a stable solution. The design matrix for solutions is usually ill-conditioned in the experiments. To solve this unstable solution problem, regularization techniques are generally used. In this paper, we describe the effective determination of the optimal regularization parameter in the regularization technique during RPC derivation. A brief mathematical background of RFM is presented, followed by numerical approaches for effective determination of the optimal regularization parameter using the Euler Method. Experiments are performed assuming that a tilted aerial image is taken with a known rigorous sensor. To show the effectiveness, calculation time and RMSE between L-curve method and proposed method is compared.