• Korean Journal of Social Welfare
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• v.61 no.1
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• pp.5-32
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• 2009

The purpose of this study is to examine the effects of organizational culture types (rational culture, developmental culture, group culture, and hierarchical culture) on Community-Centered Social Inclusion Orientation(COSI) among social service organizations for people with disabilities. More specifically, this study developed and empirically tested two theoretical models where each of organizational culture types were hypothesized to affect COSI either directly or indirectly through market orientation. For data collection, self-administered type questionnaires were mailed to 416 social service organizations for people with disabilities across the nation and 293 responded (response rate: 70.4%) from June 1 to September 22, 2006. For analysis, however, only 263 respondents were used, excluding Independent living centers due to their small size and short history. Structural Equation Modeling was employed for analysis and Full Information Maximum Likelihood was used for estimation. Findings indicated that market orientation had a significant effect on COSI. In addition, developmental culture and hierarchical culture were found to affect COSI directly while rational culture and group culture were found to affect COSI indirectly through market orientation. These findings imply that market orientation needs to be emphasized as a strategy in order to enhance social inclusion orientation for those organizations. Given that all the four types of organizational culture have direct or indirect impacts on COSI, those organizations are also advised to develop the four types of organizational culture harmoniously rather than one single type of organizational culture.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.295-302
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• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2179-2188
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• 2015

Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the “Two-step” method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.

• Journal of Korea Technology Innovation Society
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• v.20 no.3
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• pp.664-683
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• 2017

The technology transfers from public institutions have positioned themselves as knowledge conduits for stimulating firms' capabilities under 'catching-up' economy paradigm. While this view is still relevant, the policy for public technology needs to be extended to a more marketoriented way. This study explores how public technology affects the financial performance of demanding companies by comparing the revenues and profits of 'technology commercialised group (TC)' and 'in-house research group (InR)'. The estimation results by hierarchy regression suggest that the size and the patents of TC firms have a more influential impact than InR group, although the ratio of maintaining research staff was reported an inverse relation. The contribution to the operating profit seems to be indifferent between the groups. The positive impact of public technology over the firm's performance is highly related to the technology commercialisation capability, signalling that the aim of technology transfer needs to gear toward the enhancement of commercialising capabilities rather than the promotion of technology transactions.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.859-869
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• 2012

The Multi-view Video Coding (MVC) which is exploiting disparities between views has been developed to improve the coding efficiency of multi-view video. But MVC has a problem of having high computing complexities because of disparity estimation. This paper propose a fast mode decision for non-anchor picture to reduce the computational time of MVC. The proposed method uses two phases. Anchor pictures in hierarchical B picture structure have a higher correlation with prediction mode selection of non-anchor pictures, so in the first phase, prediction mode of non-anchor pictures is selected by exploiting the macro-block regions in anchor picture. In the second phase, we select a reference direction of inter prediction mode exploiting a higher correlation among reference directions of inter prediction modes of 7 block sizes. Experimental results show that the proposed method could save average about 44% in the encoding time with negligible coding efficiency losses.

• Journal of Animal Science and Technology
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• v.55 no.6
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• pp.489-500
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• 2013

Daily milk yields on test days were estimated using morning or afternoon partial milk yields collected by official agencies and the accuracy of the estimates was determined. Test-day data for milk yields consisted of 3,156,734 records of AM/PM partial milking measurements of 255,437 milking Holstein cows from 3,708 farms collected from December 2008 to April 2013. A linear regression model (LRM) was applied to estimate daily milk yields using alternate AM/PM milk yield records within lactation stages, milking intervals, and parities on every daily milk yield. The alternate statistical approach was a non-linear hierarchical model (NHM) in which Brody's growth function was implemented by reflecting an animal's physiological milk production cycle. When compared with LRM, daily milk yields predicted by the NHM were assumed to be functionally related to day in milk (or lactation) stage, milking intervals, and partial milk yields. Since the results were in terms of accuracies based on comparisons of different statistical models, accuracies of estimates of daily milk yields by NHM were close to those determined by the LRM. The average of these accuracies was 0.94 for AM partial milk yields and 0.93 for PM partial milk yields for first calving cows. However, the accuracies of AM/PM milk yield estimations from cows under a calving stage higher than the first parity were 0.96 and 0.95, respectively. Correlations between the estimated daily milk yields and the actual daily milk yields ranged from 0.96~0.98. These accuracies were lower for unbalanced AM/PM milking intervals and the first calving cows. Overall, prediction of daily milk yields by NHM would be more appropriate than by LRM due to its flexibility under different milk yield-related circumstances, which provides an idea of the functional relationship between milking intervals and days in milk with daily milk yields from statistical viewpoints.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.961-975
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• 2016

The hierarchically penalized support vector machine (H-SVM) has been developed to perform simultaneous classification and input variable selection when input variables are naturally grouped or generated by factors. However, the H-SVM may suffer from estimation inefficiency because it applies the same amount of shrinkage to each variable without assessing its relative importance. In addition, when analyzing imbalanced data with uneven class sizes, the classification accuracy of the H-SVM may drop significantly in predicting minority class because its classifiers are undesirably biased toward the majority class. To remedy such problems, we propose the weighted adaptive H-SVM (WAH-SVM) method, which uses a adaptive tuning parameters to improve the performance of variable selection and the weights to differentiate the misclassification of data points between classes. Numerical results are presented to demonstrate the competitive performance of the proposed WAH-SVM over existing SVM methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1120-1131
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• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• Safety and Health at Work
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• v.7 no.1
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• pp.6-11
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• 2016

Background: A permit to work (PTW) is a formal written system to control certain types of work which are identified as potentially hazardous. However, human error in PTW processes can lead to an accident. Methods: This cross-sectional, descriptive study was conducted to estimate the probability of human errors in PTWprocesses in a chemical plant in Iran. In the first stage, through interviewing the personnel and studying the procedure in the plant, the PTW process was analyzed using the hierarchical task analysis technique. In doing so, PTWwas considered as a goal and detailed tasks to achieve the goal were analyzed. In the next step, the standardized plant analysis risk-human (SPAR-H) reliability analysis method was applied for estimation of human error probability. Results: The mean probability of human error in the PTW system was estimated to be 0.11. The highest probability of human error in the PTW process was related to flammable gas testing (50.7%). Conclusion: The SPAR-H method applied in this study could analyze and quantify the potential human errors and extract the required measures for reducing the error probabilities in PTW system. Some suggestions to reduce the likelihood of errors, especially in the field of modifying the performance shaping factors and dependencies among tasks are provided.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.111-118
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• 2008

In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.