• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.25-36
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• 2004

The effects of task characteristics on the relative efficiency of visual display dimension were studied using a simulated tele-robotic task. Through a conventional method of task analysis. the tele-robotic task was divided into two categories: the task element requiring focused attention (FA task) and the task element requiring global attention (CA task). Time-ta-completion data were collected for a total of 120 trials involving 10 participants. For the CA task. there was no significant difference between the multiple two-dimensional (20) display and the three-dimensional (3D) monocular display. For the FA task. however. the multiple 20 display was superior to the 3D monocular display. The results suggest that the characteristics of a given task have a considerable effect on the choice of display dimensionality and the multiple 3D display is better for human operators to effectively judge depth if the task requires frequent use of focused attention.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.193-202
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• 2010

The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers' explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer's navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is 'large.' In addition, the proposed approach requires less memory space and is computationally more efficient.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.58-71
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• 2019

The purpose of this study is to explore the multi-dimensionality and the differences of the career success that is revealed by the employee's perception. In order to fulfill the research purpose, LDA topic modeling has applied to extract latent topics of career success from 126 Korean employees' open-end survey questionnaires. The extracted latent topics are social recognition, continuing service within an organization, expertise, financial rewards, and pursuing personal meaning. The occurrence probability of each topic was different by individual characteristics such as gender, education, position. Study findings showed there is multi-dimensionality in career success, and there are differences of topic occurrence probability by demographic characteristics. Additionally, this study showed how to apply the recently developed machine learning approach in order to reduce the researcher's bias by adapting the LDA topic modeling to the qualitative open-ended survey data.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.5
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• pp.746-754
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• 1987

A method for measuring three-dimensional turbulent flows by the hot-wire anemometer is introduced. Mojolla's method using the X-type probe is adopted and modified for the slantwire probe without the linearizer. The probe is aligned with specified angles to the given uniform flow and the shear layer to verify the measuring errors due to the three-dimensionality and the turbulence level. Errors in the measurements of mean velocities and Reynolds stresses increase with the degree of three dimensionality in the flow. The incoming flow angle of 20 degree seems to be the limit of reasonable flow measurements. But there still appear large data scatterings in Reynolds shear stresses.

• ETRI Journal
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• v.36 no.4
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.250-254
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• 2010

As the number of input and fuzzy set of a fuzzy system increase, the size of the rule base increases exponentially and becomes unmanageable (curse of dimensionality). In this paper, tree architectures of fuzzy controller (TAFC) is proposed to overcome the curse of dimensionality problem occurring in the design of fuzzy controller. TAFC is constructed with the aid of AND and OR fuzzy neurons. TAFC can guarantee reduced size of rule base with reasonable performance. For the development of TAFC, genetic algorithm constructs the binary tree structure by optimally selecting the nodes and leaves, and then random signal-based learning further refines the binary connections (two-step optimization). An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.192-197
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• 2020

Foreign students in South Korea face important challenges when they try to maintain their health. As a measure of their motivation to actively build skills for overcoming those challenges, we evaluated the 10-item Consumer Health Activation Index (CHAI), testing its feasibility, dimensionality, and reliability. There were no missing data, there was no floor effect, and for the total scores the ceiling effect was trivial (< 2%). Results of the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity indicated that the data were suitable for the detection of structure by factor analysis. The results of parallel analysis and the shape of the scree plot supported a two-factor solution. One factor had 3 items concerning "my doctor" and the other factor had the 7 remaining items. Reliability was high for the 10-item CHAI (alpha = 0.856), for the 3-item subscale (alpha = 0.838), and for the 7-item subscale (alpha = 0.857). Reliability could not be improved by deletion of any items. Use of the CHAI to gather data from these foreign students is feasible, and reliable results can be obtained whether one uses the total score from all 10 items or scores from the proposed 7-item and 3-item subscales.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.959-966
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• 2000

A dimensionality assessment procedure DETECT uses the property of being near zero of conditional covariances as an indication of unidimensionality .This study provides the convergent properties to zero of conditional covariances when the dta is unidimensional, with which DETECT extends its theoretical grounds.