• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• School Mathematics
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• v.13 no.3
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• pp.363-384
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• 2011

This study is based on the recognition that the school mathematics education should reinforce the heuristic and constructional aspects related with discoveries of mathematical rules and understanding of mathematical concepts from real world situations as well as the deductive and formal aspects emphasizing on mathematical contents precisely. The 11th grade students of one class from a city high school with average were chosen. They were given time to learn various functions of Excel in regular classes of "Information Society and Computer" subject. They don't have difficulty using cells, mathematical functions and statistical functions in spreadsheet. Experiment was performed for six weeks and there were two hours of classes in a week. Considering the results of this research, teaching materials using spreadsheets play an important role in helping students to experience probabilistic and statistical reasoning and construct mathematical thinking. This implies that teaching materials using spreadsheet provide students with an opportunity to interact with probabilistic and statistical situations by adopting engineering which can encourage students to observe and experience various aspects of real world in authentic situations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.2
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• pp.149-158
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• 2005

In this paper, we have designed and implemented a portable e-tongue (electronic tongue) system using MACS (multi array chemical sensor) and PDA. The system embedded in PDA has merits such as comfortable user interface and data transfer by internet from on-site to remote computer. MACS was made up 7 electrodes (${NH_4}^+$, $Na^+$, $Cl^-$, ${NO_3}^-$, $K^+$, $Ca^{2+}$, $Na^+$, pH) and a reference electrode. For learning the system, we adapted the Levenberg-Marquardt algorithm based on the back-propagation, which could iteratively learned the pre-determined standard patterns, in e-tongue system. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. The e-tongue was applied to whiskeys and cognac (one high level whisky, one low level whiskey, two cognac) and 2 sample whiskeys for each standard patterns and unknown patterns. The relationship between the standard patterns and unknown patterns can be easily analyzed.

• Journal of Engineering Education Research
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• v.13 no.6
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• pp.132-142
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• 2010

The purpose of this study is to suggest the assessment method for PO6(teamwork) and PO7(communication skill), one of the most important assessment items in engineering accreditation. In this paper, we used the assessment criteria of Rubric, to empirically measure the teamwork and communication skill. Teamwork and communication skill (PO6, PO7) are the most critical social competences in modern society. Numerous studies on education showed that teamwork and communication skill can be enhanced through learning. We, therefore, need to investigate teamwork and communication skill in terms of education. However, research on the assessment method of the engineering accreditation, based on educational view is scarce. In this study, we suggest the assessment criteria of Rubric for PO6 and PO7 with the perspective of the education. We also tried to apply the developed criteria to the related subjects.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Safety and Health at Work
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• v.4 no.3
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• pp.166-176
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• 2013

Background: Effective policy implementation is essential for a healthy workplace. The Ryan-Kossek 2008 model for work-life policy adoption suggests that supervisors as gatekeepers between employer and employee need to know how to support and communicate benefit regulations. This article describes a workplace intervention on a national employee benefit, Family and Medical Leave Act (FMLA), and evaluates the effectiveness of the intervention on supervisor knowledge, awareness, and experience with FMLA. Methods: The intervention consisted of computer-based training (CBT) and a survey measuring awareness and experience with FMLA. The training was administered to 793 county government supervisors in the state of Oregon, USA. Results: More than 35% of supervisors reported no previous training on FMLA and the training pre-test revealed a lack of knowledge regarding benefit coverage and employer responsibilities. The CBT achieved: (1) a significant learning effect and large effect size of d = 2.0, (2) a positive reaction to the training and its design, and (3) evidence of increased knowledge and awareness regarding FMLA. Conclusion: CBT is an effective strategy to increase supervisors' knowledge and awareness to support policy implementation. The lack of supervisor training and knowledge of an important but complex employee benefit exposes a serious impediment to effective policy implementation and may lead to negative outcomes for the organization and the employee, supporting the Ryan-Kossek model. The results further demonstrate that long-time employees need supplementary training on complex workplace policies such as FMLA.

• Proceedings of the IEEK Conference
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• summer
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• pp.410-415
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• 2004

Employing knowledge of adaptive possibilities of agents in multi-agents system, we have explored new aspects of distributed modular systems for building ubiquitous heterogeneous robotic systems using intelligent building blocks (I-BLOCKS) [1] as reconfigurable modules. This paper describes early technological approaches related to technical design, experimental developments and evaluation of adaptive processing and information interaction among I-BLOCKS allowing users to easily develop modular robotic systems. The processing technology presented in this paper is embedded inside each $DUPLO^1$ brick by microprocessor as well as selected sensors and actuators in addition. Behaviors of an I-BLOCKS modular structure are defined by the internal processing functionality of each I-Block in such structure and communication capacities between I-BLOCKS. Users of the I-BLOCKS system can easily do 'programming by building' and thereby create specific functionalities of a modular robotic structure of intelligent artefacts without the need to learn and use traditional programming language. From investigating different effects of modern artificial intelligence, I-BLOCKS we have developed might possibly contain potential possibilities for developing modular robotic system with different types of morphology, functionality and behavior. To assess these potential I-BLOCKS possibilities, the paper presents a limited range of different experimental scenarios in which I-BLOCKS have been used to set-up reconfigurable modular robots. The paper also reports briefly about earlier experiments of I-BLOCKS created on users' natural inspiration by a just defined concept of modular artefacts.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.211-218
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• 2010

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. Moreover, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and design (control) factors, and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network without resorting to SN. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.