• Asia pacific journal of information systems
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• v.18 no.1
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• pp.53-77
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• 2008

In recent years, although numbers of corporations are bringing in PMO, they seem to be indifferent to PMO performance measurement. This demonstrates that there are also other reasons beside performance measurement of information systems (IS) project being ambiguous by introducing PMO; the lack of acknowledging the concrete function of PMO, and the scarcity of empirical study about the effect of PMO on the project members and project performance. In this sense, this study is aimed at proposing a new research model in which project success factors (i.e., standardization, management advocacy, and staff expertise) affect PMO capability (i.e., knowledge management, resources management, and problem solving competency) positively, leading to project performance (i.e., task outcomes, psychological outcomes, and organizational outcomes) eventually. To empirically test the research model, data are surveyed from PMO department and IS department. To prove the validity of the proposed research model, PLS analysis is applied with valid 132 questionnaires. By employing PLS technique, the measurement reliability and validity of research variables are tested and the path analysis is conducted to do the hypothesis testing. The path analysis results can be organized into 7 ways in large scale. First, standardization of project success factors has a positive association with knowledge management, resources management, and problem solving competency of PMO capabilities. The findings of this result indicate that the multiple or single project management should satisfy standardization in order to operate an effective PMO. Second, management advocacy of project success factors has a positive association with knowledge management, resources management, and problem solving competency. Management advocacy refers to the willingness of management to provide the required resources and authority for project success. There is agreement among researchers regarding the importance of management advocacy for favorable PMO capability. Third, staff expertise of project success factors has a positive association with knowledge management, resources management, and problem solving competency. The findings of this result indicate that the formation of an exceptional consultant or members with a proficient knowledge for staff expertise of project member is the key factor to elevate the PMO capability. Past research suggests that experience and knowledge and the resultant familiarity with the problem faced can be an important determinant of PMO capability. A capable project with appropriate staff expertise means that it enjoys a diversity of abilities and experiences. Fourth, knowledge management competency of PMO capabilities has a positive impact on psychological outcomes but has no direct effect on task outcomes and organizational outcomes. In domestic case of S. Korea, PMO was finally introduced to many other corporations in 2005 though it started bringing in 2000. Therefore, it had neither a significant impact on the task outcomes nor organizational outcomes by lacking the contents and the infrastructure of the knowledge management because the knowledge consolidation and management period of PMO is comparatively shorter by terms than other foreign nations. Fifth, resources management competency of PMO capabilities has a positive association with task outcomes, psychological outcomes, and organizational outcomes. In addition, problem solving competency of PMO capabilities has a positive association with task outcomes, psychological outcomes, and organizational outcomes. Therefore, the findings of this results stress that PMO capabilities has a positive impact on project performance. Sixth, according to the path analysis of the hypothesis, which suggested in this research, problem solving competency is the PMO capability which is the key success factor for task, psychological, and organizational outcomes as an integrated performance model. Further, the analysis reveals that problem solving competency is an important factor for integrated performance model. The finding is in line with past IS research, which affirms that the work of IS projects is essentially a problem solving endeavor. Seventh, in the path analysis of the hypothesis in this research, the path of the management advocacy $\rightarrow$ problem solving competency $\rightarrow$ organizational outcomes appears to be the most important and strongest path. In brief, the finding of this study suggests that project success factors influence PMO capability positively, and project performance as well. From the results, it can be concluded that PMO helped great improve the project success rate and project performance. This study advances research on PMO capability in three important aspects. First, the findings of our study have implications for IS theory and future research. Our study contributes to IS theory by synthesizing concepts from PMO research and project management research with those in IS research. We proposed and tested PMO capability of IS projects and the findings of our investigation provided some preliminary answers to some of the questions raised. Secondly, this thesis does not only help depicting the concept of IT governance but also approaches empirically. It makes a gradual approach to the main content, step by step, in contrary of simple standard, scholastic way of thinking. Finally, we argued that this task-oriented(technical) view is not sufficient to adequately conceptualize IS project performance. Hence, we applied that the research on organization teams, which provides a flip viewpoint to that of project management research in that it gives more weight for psychological outcomes of organizational work groups, can be very helpful in reconceptualizing the IS project performance construct. The limitations of this study are also discussed to provide research directions for future research.

• Journal of Gifted/Talented Education
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• v.7 no.1
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• pp.77-116
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• 1997

The paper-and-pencil multiplce choise test has been widely used to identify gifted and talented student in Korea. Such test have several drawbacks; such~a s not being able to assess creative problem solving abilities and high level thinking abilities which are important characteristics of gifted students. The Korean Educational Development Institute (KEDI) developed a performance assessment which challenged traditional methods of identification. The 5-day summer camp was held to select gifted students for the Korean Minjok Leadership Academy. 211 students were evaluated in the aspects of creative problem solving abilities, high level thinking abilities, task commitments, and cooperativeness with various performance tasks, such as essay tests, conversations, oral examinations, computer simulations, puzzles, experiments, group discussions, debates, research reports, and games. As a result, it was found that there were several limitations of the performance assessment in terms of low reliability, requiring high costs and many professionals, and taking long times and large spaces. Expanding and continuing research should follow in order for a performance assessment to use widely as an identification methods because it assesses 'true' abilities of each individual student. follow in order for a performance assessment to use widely as an identification methods because it assesses 'true' abilities of each individual student.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.107-112
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• 2020

We analyze the flow aggregate (FA) based network delay guarantee framework, with generalized minimal interleaved regulator (IR) initially suggested by IEEE 802.1 time sensitive network (TSN) task group (TG). The framework has multiple networks with minimal IRs attached at their output ports for suppressing the burst cascades, with FAs within a network for alleviating the scheduling complexity. We analyze the framework with various topology and parameter sets with the conclusion that the FA-based framework with low complexity can yield better performance than the integrated services (IntServ) system with high complexity, especially with large network size and large FA size.

• ETRI Journal
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• v.42 no.2
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.351-361
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• 2010

The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods are considered the preferred methods. Selecting a suitable preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The prediction of ILU type preconditioners was considered in [27] where support vector machine(SVM), as a data mining technique, is used to classify large sparse linear systems and predict best preconditioners. In this paper, we apply the data mining approach to the sparse approximate inverse(SAI) type preconditioners to find some parameters with which the preconditioned Krylov subspace method on the linear systems shows best performance.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.5
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• pp.322-327
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• 2010

It is a critical task to keep the ventilation system working in a proper and efficient manner in large multi-storey buildings, and the enthalpy exchanger is becoming an increasingly important part of the ventilation system by playing the function of channeling heat and moisture. We present a computational study on the heat transfer performance of the cross-flow enthalpy exchanger, which is in large use for residential buildings. The ducts are considered whose cross-sectional shapes resemble triangle and longitudinal centerline a cosine wave. It is shown that, as the cross-sectional shape departs from triangle, the heat transfer performance of the duct tends to deteriorate. Also, applying the wave-like shape to the longitudinal centerline of the duct increases the rate of heat transfer and the applied pressure-gradient at the same time. The origin of the performance variations in the cases considered are quantitatively analyzed and discussed.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.10-22
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• 2021

Purpose: To overcome the difficulty in building a large data set with a high-quality in medical imaging, a concept of 'blended-transfer learning' (BTL) using a combination of both source data and target data is proposed for the target task. Materials and Methods: Source and target tasks were defined as training of the source and target networks to reconstruct cardiac CINE images from undersampled data, respectively. In transfer learning (TL), the entire neural network (NN) or some parts of the NN after conducting a source task using an open data set was adopted in the target network as the initial network to improve the learning speed and the performance of the target task. Using BTL, an NN effectively learned the target data while preserving knowledge from the source data to the maximum extent possible. The ratio of the source data to the target data was reduced stepwise from 1 in the initial stage to 0 in the final stage. Results: NN that performed BTL showed an improved performance compared to those that performed TL or standalone learning (SL). Generalization of NN was also better achieved. The learning curve was evaluated using normalized mean square error (NMSE) of reconstructed images for both target data and source data. BTL reduced the learning time by 1.25 to 100 times and provided better image quality. Its NMSE was 3% to 8% lower than with SL. Conclusion: The NN that performed the proposed BTL showed the best performance in terms of learning speed and learning curve. It also showed the highest reconstructed-image quality with the lowest NMSE for the test data set. Thus, BTL is an effective way of learning for NNs in the medical-imaging domain where both quality and quantity of data are always limited.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.153-162
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• 2017

Advances in communication technologies and the decreasing cost of computers have made distributed computer systems an attractive alternative for satisfying the information needs of large organizations. This paper presents a distributed algorithm for performance improvement through load balancing and file migration in distributed systems. We employed a sender initiated strategy for task migration and used learning automata with several internal states for file migration. A task can be migrated according to the load information of a computer. A file is migrated to the destination processor when it is in the right boundary state. We also described an analytical model for load balancing with file migration to verify the proposed algorithm. Analytical and simulation results show that our algorithm is very well-suited for distributed system environments.