• Educational Technology International
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• 제10권1호
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• pp.25-40
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• 2009

The purpose of this study was to explore procedures and modalities on collaborative visualization processing of text information for developing e-Learning contents. In order to investigate, two research questions were explored: 1) what are procedures on collaborative visualization processing of text information, 2) what kinds of patterns and modalities can be found in each procedure of collaborative visualization of text information. This research method was employed a qualitative research approaches by means of grounded theory. As a result of this research, collaborative visualization processing of text information were emerged six steps: identifying text, analyzing text, exploring visual clues, creating visuals, discussing visuals, elaborating visuals, and creating visuals. Collaborative visualization processing of text information came out the characteristic of systemic and systematic system like spiral sequencing. Also, another result of this study, modalities in collaborative visualization processing of text information was divided two dimensions: individual processing by internal representation, social processing by external representation. This case study suggested that collaborative visualization strategy has full possibility of providing ideal methods for sharing cognitive system or thinking system as using human visual intelligence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4510-4526
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• 2017

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Current Optics and Photonics
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• 제4권3호
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.65-70
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• 2005

This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

• 인터넷정보학회논문지
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• 제11권5호
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• pp.95-104
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• 2010

학습을 이해하는 새로운 패러다임으로 구성주의가 등장하면서 협력학습의 필요성이 강조되고 있다. 특히 교수와 학습에 대한 새로운 접근을 지원하는 테크놀로지로 온라인이 부각되면서 온라인 협력학습에 대한 관심이 증대하고 있다. 본 연구는 온라인 협력학습에서 하나의 주요한 요인인 학습과제 유형에 따른 협력학습 모형을 탐색하여 온라인 협력학습 실제에 도움을 주고자 하였다. 이를 위해 학습과제를 문제해결과제와 지식학습과제로 분류한 후, 학습과제 유형별로 적합한 온라인 협력학습 설계와 환경, 그리고 학습과정을 살펴보았다.

• 산업경영시스템학회지
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• 제33권3호
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• pp.87-92
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• 2010

In this paper, we suggest a PLCS-based architecture and operation method for providing interoperability in SBA integrated collaborative environment. Specifically, the suggested architecture is based on the distributed collaborative environment which employes the PLCS application protocol and integrated repository for representing and sharing product data information between collaborators remotely located. As an example of data representation, a military vehicle system is considered and two kinds of information, including state and activity/process, are explained. We expect that the suggested architecture can be used as a reference model to develop an efficient SBA integrated collaborative environment.

• 컴퓨터교육학회논문지
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• 제19권2호
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• pp.73-85
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• 2016

컴퓨터기반 협력학습도구를 사용하여 한 공간에서 다양한 관점을 가진 사람들이 토의하고자 할 때, 작성된 글이 과제내용 중 어느 부분에 해당하는 것인지를 파악하는 것이 어렵고 서로의 지식과 의견을 공유하는데 어려움이 따른다. 본 연구에서는 컴퓨터 기반 협력학습에서 공유지식 형성을 방해하는 요인을 문헌연구를 통해 분석하고 도출된 원리를 기반으로 협력표상도구를 설계하였다. 설계된 도구는 평가 준거에 따른 체크리스트와 F.G.I를 통해 교수자, 설계자, 학습자의 다양한 의견을 수렴함으로써 반복 조정되었다. 최종 조정된 도구는 복합 과제를 해결해야하는 컴퓨터 기반 협력학습상황에서 학습자 간 지식 및 의견을 공유하는 데 방해요소를 최소화하여 협의를 촉진하고 고차원의 해결책을 도출하는 데 기여할 것이라 기대한다.

• Educational Technology International
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• 제9권2호
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• pp.79-95
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• 2008

This paper aims to investigate modeling scripts based on the mode of group interaction in a computer-supported collaborative learning environment. Based on a literature review, this paper assumes that group interaction and its mode would have strong influence on the online collaborative learning process, and furthermore lead learners to create and share significant knowledge within a group. This paper deals with two different modes of group interaction- distributed and shared interaction. Distributed interaction depends on the external representation of individual knowledge, while shared interaction is concerned with sharing knowledge in group action. In order to facilitate these group interactions, this paper emphasizes the utilization of appropriate CSCL scripts, and then proposes the conceptual framework of CSCL scripts which integrate the existing scripts such as implicit, explicit, internal and external scripts. By means of the model regarding CSCL scripts based on the mode of group interaction, the implications for research on the design of CSCL scripts are explored.