• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.4
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• pp.105-118
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• 2007

The purpose of this study is not only to compare between 'Opened Landscape' and 'Filtered Landscape' image and preference but also to suggests a guide line of planting design for progressive realization. For this, the image structures of photo-sketch simulation for parallax landscape have been investigated by Semantic Differential scale(S.D. scale) and the Factor analysis. The results could be summarized as follows. The results of S.D. scale values for landscape through parallax were greater than non-parallax landscape. The scenes through parallax were better preferred to direct view. Thus the results of photo-sketch simulation test support the expected hypothesis that the visual environment of complexity and variety is closely correlated with the parallax effect and monotonous or non-parallax environment, and parallax effect on close view more bigger than the distant view. Factors covering the spatial image of parallax landscape were found to be seven and Total values were 60.35 %. The most important factors determining the parallax effect were Factors I 'depth of space' and VI 'expectation of space and interest'. An outstanding view must be handled properly to be preserved or accentuated. In this sense, the parallax spatial beauty with tree could be improved through the visual aspects of plan arrangements and the progressive realization appeared to be one effective design technique for landscape planning and design.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.90-93
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• 2008

In natural language, it is common that repetitive constituents in an expression are to be left out and it is necessary to figure out the constituents omitted at analyzing the meaning of the sentence. This paper is on recognition of boundaries of parallel noun phrases by figuring out constituents omitted. Recognition of parallel noun phrases can greatly reduce complexity at the phase of sentence parsing. Moreover, in natural language information retrieval, recognition of noun with modifiers can play an important role in making indexes. We propose an unsupervised probabilistic model that identifies parallel cores as well as boundaries of parallel noun phrases conjoined by a conjunctive particle. It is based on the idea of swapping constituents, utilizing symmetry (two or more identical constituents are repeated) and reversibility (the order of constituents is changeable) in parallel structure. Semantic features of the modifiers around parallel noun phrase, are also used the probabilistic swapping model. The model is language-independent and in this paper presented on parallel noun phrases in Korean language. Experiment shows that our probabilistic model outperforms symmetry-based model and supervised machine learning based approaches.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.13-20
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• 2007

The aim of this study was to investigate effects of odor intensity on the olfactory sensibility and sensibility structure. Three odor samples(B, C, and D) of T&T olfactometer were selected by the preference rank : the lowest preference(C) ; the moderate one(B) ; and the highest one(D). Three levels(-1, +1, and +3) of odor intensity at each sample were presented to 50 subjects(25 female, 25 male), and the olfactory sensibility was rated by using semantic differential scale composed 25 sensibility characteristics. At each sample, the olfactory sensibility was significantly affected by the odor intensity. Moreover, the structure of olfactory sensibility was influenced by the odor intensity. However, two sensibility factors such as 'aesthetics' and 'intensity' were common factors, whereas 'mildness', 'complexity', and 'activity' were unique factors with odor intensity. In conclusion, the olfactory sensibility was significantly affected by the odor intensity and the odor preference.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• Journal of KIBIM
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• v.12 no.2
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• pp.12-25
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• 2022

To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.284-291
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• 2022

Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.30-33
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• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.

• Land and Housing Review
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• v.2 no.3
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• pp.249-258
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• 2011

In order to achieve an efficient urban regeneration of the nation, it is required to determine the extent of decline nation-wide and the declined areas for each district and also to evaluate the potentials of the concerned areas. For this task to be accomplished, a construction of a comprehensive diagnostic system based on spatial information considering diversity and complexity is required. In this study, a total information system architecture for urban regeneration is designed as part of the construction of such a diagnostic system. In order to develop the system, a city and district level unit decline diagnostic indicators has been constructed and a decline diagnostic system has been developed. Also, a scheme to promote the advancement of the system is proposed. The DB construction is based on the city and district level nation-wide and metadata for the concerned level is constructed as well. The system is based on the Open API and designed to be flexible for extension. Also, an RIA-based intuitive UI has been implemented. Main features of the system consist of the management of the indicators, diagnostic analysis (city and district level decline diagnosis), related information, etc. As for methods for the advancement, an information model in consideration of the spation relations of the urban regeneration DB has been designed and application methods of semantic webs. Also, for improvement methods for district unit analytical model, district level analysis models, GIS based spatial analysis platforms and linked utiliation of KOPSS analysis modules are suggested. A use of a total information system for urban regeneration is anticipated to facilitate concerned policy making through the identification of the status of city declines to identify and the understanding of the demands for regeneration.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.528-545
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• 2007

Recently ontology has been an attractive technology along with the business strategy of providing a plenty of more intelligent services. The essential problem in application domains using ontology is that all members, agents, and application programs in the domains must share the same ontology concepts. However, a variety of mobile devices, sensing devices, and network components manufactured by various companies, a variety of common carriers, and a variety of contents providers make multiple heterogeneous ontologies more likely to coexist. We can see many past researches fallen into resolving this semantic interoperability. Such methods can be broadly classified into by-mapping, by-merging, and by-translation. In this research, we focus on by-translation among them which uses a translation rule directly made between two heterogeneous ontology data like OntoMorph. However, the manual composition of the direct translation rule is not convenient by itself and if there are N ontologies, the direct method has the rule composition complexity of $O(N^2)$ in the worst case. Therefore, in this paper we introduce the cascade composition of translation rules based on web openness in order to improve the complexity. The research result made us recognize some important factors in an ontology translation system, that is speediness of translation, and conveniency of translation rule composition, and some experiments and comparing analysis with existing methods showed that our cascade method has more conveniency with insuring the speediness and the correctness.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.33-48
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• 2005

The purpose of this study was to discover the landscape visual image of the slope scene and their harmony with surrounding sceneries. This research utilized the basic study tool of psycho-physics and processed the case study of ten types of slope construction scene along the highway. The analysis was performed by the data obtained from the questionnaires and the photos for the slope construction scene. The questionnaires for analysis the image of the slope construction scene and their harmony with surrounding sceneries were designed using semantic differential scale and 5 point Likert-scale. The major findings were as follows. 1. At the part of the visual preferences analysis, the slope revegetation methods showed high level of preferences generally than on the slope structure methods. While the slope revegetation methods were estimated friendly, continuity, harmonious, soft, light and wide, the slope revegetation methods were estimated unstable, female, static, simple, omnipresent, appeared as policeman of weak inclination. Also the slope structure methods were estimated stable, manly, complicated, steep and healthy but rough, unharmonious, unfamiliar and heavy. 2. Psychological factors, related to the satisfaction for the slope revegetation methods were composed of three factors, aesthetic, individuality and physical character. And the slope structure methods were composed of five factors, aesthetic, individuality, stability, physical character, and complexity. 3. At the part of harmony with surrounding landscapes, the slope revegetation methods were evaluated highly but the slope structure methods received the lowest evaluation. Also the harmony analysis with surrounding view on the slope revegetation methods showed degree of high more than average in all texture, form, color and scale but the slope structure methods showed degree of fewer than average degree in form, scale, color and texture.