• Journal of Korea Technology Innovation Society
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• v.8 no.2
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• pp.605-622
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• 2005

Because, next generation web, semantic web consists of documents with semantic information, it enables computer interpret the contents of the documents, so that the information retrieval, interpretation and integration can be automated. The web documents with the semantic information may be made in ontology. In this paper, collaborative approach among the ontology design techniques is more excellent than the other techniques because it design the ontology through continuous evaluations and modification in terms of multiple views. So, we propose the process of designing and implementing the ontology for specific domain, which is Yeomigi tour place. Delphi technique, that is a kind of collaborative approach, is used when the ontology is designed.

• Research in Mathematical Education
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• v.15 no.1
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• pp.15-30
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• 2011

We study to design educational Logo-based microworld environment equipped with 3D construction capability, 3D manipulation, and web-based communication. Extending the turtle metaphor of 2D Logo, we design simple and intuitive symbolic representation system that can create several turtle objects and operations. We also present various mathematization activities applying the turtle objects and suggest the way to make good use of them in mathematics education. In our microworld environment, the symbolic representations constructing the turtle objects can be used for web-based collaborative learning, communication, and assessments.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.35-49
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• 1997

The usability of the Internet including WWW (World Wide Web) is dramatically growing in current business environment. These allow decision makers to enhance the productivity of decision making by referring valuable information in the remote sites, This paper presents the possibilities how WWW can be applied to build distributed and collaborative DSS, especially model management subsystem. A framework of Internet-based DSS is delineated, and then an idea of representing and managing models in the Internet-based DSS is suggested.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1313-1321
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• 2017

In the future society where ICT-based digital convergence creates new value, collaborative skills among experts in various fields and SW based problem solving ability is more emphasized. Non-SW specialists are required to have SW based communication skills to effectively collaborate with SW experts to solve their problems. Therefore, SW programming curriculum for non-major college students should be different from the existing programming education for SW-majors aiming at a high level of coding ability. It is also known that diagram-based visual representation is helpful for productive communication and collaboration. In this study, we defined the SW education objectives for the non-majors as cultivating the visual programming ability for SW based problem solving. In order to accomplish this, we explored SW programming curriculum and content development model for non-majors focusing on visual representation of SW solutions. The results of this paper will help to provide appropriate SW learning model for non-majors and to cultivate practical SW capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• IE interfaces
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• v.25 no.1
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• pp.40-51
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• 2012

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.139-148
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• 2006

In the collaborative product design environment, the communication between designers is important to capture design intents and to share a common view among the different but semantically similar terms. The Semantic Web supports integrated and uniform access to information sources and services as well as intelligent applications by the explicit representation of the semantics buried in ontology. Ontologies provide a source of shared and precisely defined terms that can be used to describe web resources and improve their accessibility to automated processes. Therefore, employing ontologies on assembly modeling makes assembly knowledge accurate and machine interpretable. In this paper, we propose a framework of semantic assembly modeling using ontologies to share design information. An assembly modeling ontology plays as a formal, explicit specification of a shared conceptualization of assembly design modeling. In this paper, implicit assembly constraints are explicitly represented using OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). The assembly ontology also captures design rationale including joint intent and spatial relationships.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2807-2814
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• 2000

In this paper, we suggest a visual language supporting collaboration with functional attributes. The visual language is a set of visual sentences that consist of object icons and operators. The object icon is a user who participates in collaboration. And, the operator means interactive relations between users according to a point of collaborative time. The functional attributes that support various computing orders provide flexibility of interactive relations on collaboration. Also, using representation both synchronous and asynchronous relations in collaboration, the visual language supports efficiently collaboration than conventional visual languages. And, functional attributes of visual sentences are analyzed using $\lambda$ expressions.