• Nuclear Engineering and Technology
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• v.45 no.2
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• pp.141-150
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• 2013

There is mounting evidence that complex computer system displays in control rooms contribute to cognitive complexity and, thus, to the probability of human error. Research shows that reaction time increases and response accuracy decreases as the number of elements in the display screen increase. However, in terms of supporting the control room operator, approaches focusing on addressing display complexity solely in terms of information density and its location and patterning, will fall short of delivering a properly designed interface. This paper argues that information complexity and semantic complexity are mandatory components when considering display complexity and that the addition of these concepts assists in understanding and resolving differences between designers and the preferences and performance of operators. This paper concludes that a number of simplified methods, when combined, can be used to estimate the impact that a particular display may have on the operator's ability to perform a function accurately and effectively. We present a mixed qualitative and quantitative approach and a method for complexity estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• ETRI Journal
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• v.26 no.6
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• pp.535-544
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• 2004

With video compression standards such as MPEG-4, a transmission error happens in a video-packet basis, rather than in a macroblock basis. In this context, we propose a semantic error prioritization method that determines the size of a video packet based on the importance of its contents. A video packet length is made to be short for an important area such as a facial area in order to reduce the possibility of error accumulation. To facilitate the semantic error prioritization, an efficient hardware algorithm for face tracking is proposed. The increase of hardware complexity is minimal because a motion estimation engine is efficiently re-used for face tracking. Experimental results demonstrate that the facial area is well protected with the proposed scheme.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.531-538
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• 2016

In this paper, we propose a novel post-processing method of semantic segmentation from indoor scenes with RGBD inputs. For accurate segmentation, various post-processing methods such as superpixel from color edges or Conditional Random Field (CRF) method considering neighborhood connectivity have been used, but these methods are not efficient due to high complexity and computational cost. To solve this problem, we maximize the efficiency of post processing by using depth superpixel extracted from disparity image to handle object silhouette. Our experimental results show reasonable performances compared to previous methods in the post processing of semantic segmentation.

• The Journal of Information Systems
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• v.18 no.3
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• pp.327-349
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• 2009

Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

• Language and Information
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• v.9 no.2
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• pp.107-126
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• 2005

The impersonal or psyc-predicate construction in Old English (=OE) poses a special challenge for most case theories in generative linguistics. In the OE impersonal construction, the experiencer argument is marked by dative, accusative, or nominative, whereas the theme is marked by nominative, genitive, or accusative, or by a PP. The combinations of possible cases for experiencer and theme are not random, bringing about daunting complexity for possible and impossible case frames. In this paper, I develop a conceptual semantic case theory (a la Jackendoff 1990, 1997, 2002; Yip, Maling, and Jackendoff 1987) to provide a unified account for the complicated case frames of the OE impersonal construction. In the conceptual semantic case theory, syntax and semantics have their own independent case assignment principles. For impersonal verbs in OE, I propose that UG leave an option of determining either syntactic or semantic case to lexical items. This proposal opens a new window for the OE impersonal construction, in that it naturally explains both possible and impossible case frames of the construction.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.33-62
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• 2009

Internet computing is a disruptive IT innovation. Semantic Web can be considered as an IT innovation because the Semantic Web technology possesses the potential to reduce information overload and enable semantic integration, using capabilities such as semantics and machine-processability. How should organizations adopt the Semantic Web? What factors affect the adoption and diffusion of Semantic Web innovation? Most studies on adoption and diffusion of innovation use empirical analysis as a quantitative research methodology in the post-implementation stage. There is criticism that the positivist requiring theoretical rigor can sacrifice relevance to practice. Rapid advances in technology require studies relevant to practice. In particular, it is realistically impossible to conduct quantitative approach for factors affecting adoption of the Semantic Web because the Semantic Web is in its infancy. However, in an early stage of introduction of the Semantic Web, it is necessary to give a model and some guidelines and for adoption and diffusion of the technology innovation to practitioners and researchers. Thus, the purpose of this study is to present a model of adoption and diffusion of the Semantic Web and to offer propositions as guidelines for successful adoption through a qualitative research method including multiple case studies and in-depth interviews. The researcher conducted interviews with 15 people based on face-to face and 2 interviews by telephone and e-mail to collect data to saturate the categories. Nine interviews including 2 telephone interviews were from nine user organizations adopting the technology innovation and the others were from three supply organizations. Semi-structured interviews were used to collect data. The interviews were recorded on digital voice recorder memory and subsequently transcribed verbatim. 196 pages of transcripts were obtained from about 12 hours interviews. Triangulation of evidence was achieved by examining each organization website and various documents, such as brochures and white papers. The researcher read the transcripts several times and underlined core words, phrases, or sentences. Then, data analysis used the procedure of open coding, in which the researcher forms initial categories of information about the phenomenon being studied by segmenting information. QSR NVivo version 8.0 was used to categorize sentences including similar concepts. 47 categories derived from interview data were grouped into 21 categories from which six factors were named. Five factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for improving search and integration services of the existing systems and for creating new services. Second, environmental conduciveness, reference models, uncertainty, technology maturity, potential business value, government sponsorship programs, promising prospects for technology demand, complexity and trialability affect the adoption of the Semantic Web from the perspective of technology push. Third, absorptive capacity is an important role of the adoption. Fourth, suppler's competence includes communication with and training for users, and absorptive capacity of supply organization. Fifth, over-expectance which results in the gap between user's expectation level and perceived benefits has a negative impact on the adoption of the Semantic Web. Finally, the factor including critical mass of ontology, budget. visible effects is identified as a determinant affecting routinization and infusion. The researcher suggested a model of adoption and diffusion of the Semantic Web, representing relationships between six factors and adoption/diffusion as dependent variables. Six propositions are derived from the adoption/diffusion model to offer some guidelines to practitioners and a research model to further studies. Proposition 1 : Demand pull has an influence on the adoption of the Semantic Web. Proposition 1-1 : The stronger the degree of requirements for improving existing services, the more successfully the Semantic Web is adopted. Proposition 1-2 : The stronger the degree of requirements for new services, the more successfully the Semantic Web is adopted. Proposition 2 : Technology push has an influence on the adoption of the Semantic Web. Proposition 2-1 : From the perceptive of user organizations, the technology push forces such as environmental conduciveness, reference models, potential business value, and government sponsorship programs have a positive impact on the adoption of the Semantic Web while uncertainty and lower technology maturity have a negative impact on its adoption. Proposition 2-2 : From the perceptive of suppliers, the technology push forces such as environmental conduciveness, reference models, potential business value, government sponsorship programs, and promising prospects for technology demand have a positive impact on the adoption of the Semantic Web while uncertainty, lower technology maturity, complexity and lower trialability have a negative impact on its adoption. Proposition 3 : The absorptive capacities such as organizational formal support systems, officer's or manager's competency analyzing technology characteristics, their passion or willingness, and top management support are positively associated with successful adoption of the Semantic Web innovation from the perceptive of user organizations. Proposition 4 : Supplier's competence has a positive impact on the absorptive capacities of user organizations and technology push forces. Proposition 5 : The greater the gap of expectation between users and suppliers, the later the Semantic Web is adopted. Proposition 6 : The post-adoption activities such as budget allocation, reaching critical mass, and sharing ontology to offer sustainable services are positively associated with successful routinization and infusion of the Semantic Web innovation from the perceptive of user organizations.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.123-137
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• 2007

The information space of semantic web comprised of various resources, properties, and relationships is more complex than that of WWW comprised of just documents and hyperlinks. Therefore, ranking methods in the semantic web should be modified to reflect the complexity of the information space. In this paper we propose a method of ranking query results from RDF(Resource Description Framework) knowledge bases. The ranking criterion is the importance of a resource computed based on the link structure of the RDF graph. Our method is expected to solve a few problems in the prior research including the Tightly-Knit Community Effect. We illustrate our methods using examples and discuss directions for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.248-268
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• 2018

The performance of smartphone applications are usually constrained in user interactions due to resource limitation and it promises great opportunities to improve the performance by exploring the smartphone built-in and embedded sensing techniques. However, heterogeneity in techniques, semantic gap between sensor data and usable context, and complexity of contextual situations keep the techniques from seamless integration. Relevant studies mainly focus on feasibility demonstration of emerging sensing techniques, which rarely address both general architectures and comprehensive technical solutions. Based on a proposed functional model, this investigation provides a general architecture to deal with the dynamic context for context-aware automation and decision support. In order to take advantage of the built-in sensors to improve the performance of mobile applications, an ontology-based method is employed for context modelling, linguistic variables are used for heterogeneous context presentation, and semantic distance-based rule matching is employed to customise functions to the contextual situations. A case study on mobile application authentication is conducted with smartphone built-in hardware modules. The results demonstrate the feasibility of the proposed solutions and their effectiveness in improving operational efficiency.

• Journal of the Korean Society of Costume
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• v.62 no.5
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• pp.1-15
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• 2012

In a fast changing postmodern society, contemporary fashion is becoming more complicated and ambiguous along with other genres of art than ever before. This phenomenon reigning as a sociocultural paradigm can be defined as 'indeterminacy' and it means 'undecidability'. The purpose of this study is to clarify and analyze the indeterminate characteristics of contemporary fashion reviewing the theoretical background and the architectural formativeness as a comparative research. The core idea of deconstructivism dismantles a causal relationship between function and form in fashion and the conventional notion about clothes. Complexity theory, which is the study of chaotic dynamical systems, suggests the creative idea and concept of infinite possibilities on a formative method. Meanwhile, catastrophe theory of discontinuous change can be used as interpretative strategies for the process of deconstruction and reconstruction. As a result of this study, the indeterminacy of fashion can be analyzed into five semantic categories: irregularity, immateriality, randomness, complexity and changeability. The intrinsic value of the indeterminacy in contemporary fashion is the interaction with a sociocultural ideology and a technological environment as well as an expansion of formative expression. To conclude, it can be said that the indeterminacy in fashion is a new interpretation of the relationship among body and space, clothes and society.