• Journal of Engineering Education Research
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• v.18 no.6
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• pp.64-69
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• 2015

A video-work story analysis system based on emotional state measurement includes a content provision unit which provides story content of a video-work, a display unit which displays content provided by the content provision unit, an emotional state measurement unit which measures a tense-relaxed emotional state of a viewer viewing the displayed story content, a story pattern analysis unit which analyzes the tense-relaxed emotional state measured from the emotional state measurement unit according to a scene in the story content provided by the content provision unit, and a story pattern display unit which prints out an analysis result or displays the analysis result as an image. The emotional state measurement unit measures a tense or relaxed emotional state through one or more analyses among a brainwave analysis, a vital sign analysis, or an ocular state analysis. A writer may obtain support in an additional scenario modification work, and an investor may obtain support in making a decision through the above description. Furthermore, the video-work story analysis system and analysis method based on emotional state measurement may extract a particular pattern with respect to a change in an emotional state of a viewer, compile statistics, and analyze a correlation between a story and an emotional state.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.919-925
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• 2006

Mobile IPv6 generates the loss of packets and out of sequencing when hand off, In this paper, We propose a improved hand off techniques using the mobility pattern of mobile nodes. As making group by presetting the moving range of mobile nodes, and putting buffer server in the group, the packet loss and out of packet sequence can be reduced. The proposed method prevents the out of packet sequence in If level which can be happened in the stable state, minimizes the packet re-send in TCP level. In the simulation, the proposed hand off techniques transmits packets efficiently by using the mobility pattern of mobile nodes.

• New & Renewable Energy
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• v.18 no.4
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• pp.64-69
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• 2022

Double-sided photovoltaic (PV) modules have received significant attention in recent years as a technology that can achieve higher annual energy production rates than single-sided modules. The shingled technology is a promising method for manufacturing high-density and high-power modules. These modules are divided by laser and joined with electrically conductive adhesives. The output efficiency of the divided cells depends on the division pattern and the electrode pattern, making it important to understand the output characteristics. In this study, the output characteristics of large-area double-sided light-receiving shingled cells with different split patterns and electrode patterns were investigated. The M6 size, with 6 divisions in the electrode pattern, had the highest efficiency when using 142 front fingers and 146 rear fingers. The M10 size, with 7 divisions, had the highest output when using 150 fingers equally in the front and rear. The M12 size, also with 7 divisions, showed the highest output characteristics when using 192 front fingers and 208 rear fingers.

• The Research Journal of the Costume Culture
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• v.21 no.6
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• pp.860-876
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• 2013

The pattern cases for Korean traditional socks are named "beoseonbongip" which means a pouch to keep patterns for making "beoseon". "Beoseon" is Korean traditional socks. This study is to identify characteristics of the pattern cases and to develop cultural products based on the unique characteristics of the pattern cases. One hundred fifty one photos of "beosonbongip" were collected and quantitatively and qualitatively analyzed. Seventy percent of them were made between Joseon Dynasty and 1960s. As a result, most of the collected pattern cases are rectangular and square shapes, red color, and silk fabrics, and sizes of them are from 9cm to 15cm. A few pattern cases with different sizes and colors were also observed. Most pattern cases were made by fixing two among four triangle pieces which made by folding four tips of a rectangular or square cloth and then puting a not or a loop on the remaining triangle pieces in order to open and close the pattern cases. In a small number of the pattern cases, three of the four pieces were fixed and a button, a bead, a broach, or two nots or two loops were put on the other piece for opening and closing. Products such as apparels, bags, pouches, frames, and key holders were made using "beoseonbongip" form and construction method. This shows that "beoseonbongip" is a useful motive for creative product development.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.121-130
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• 2010

In today's uncertain economic environment, economic risk is inherent in making large investments on manufacturing facilities. It is, therefore, practically meaningful to divide investment over multiple periods, reducing the risk of investment. Then, the cash-flow over the entire planning horizon would comprise positive inflow and negative outflow. In this case, in general, evaluation by internal rate of return (IRR) is not feasible, because multiple IRRs are involved. This paper deals with a problem of evaluating profitability, as well as risk, of investment alternatives made in multiple times of investment over the entire horizon. Typically, an additional investment is required after the initial one, for expanding manufacturing capacity or other reasons. The paper pays attention to a unit cash-flow over two periods, decomposing the total cash-flow into a series of unit cash-flow patterns. It is easy to evaluate profitability of a unit cash-flow by using IRR. The total cash-flow can be decomposed into the series of two types of unit cash-flows: an investment type one (negative-positive) and the borrowing type one (positive-negative). This paper, therefore, proposes a method in which only the borrowing type unit cash-flow is eliminated in the series by converting total cash-flow using capital interest rate. Then, a unique IRR can be obtained and the profitability is evaluated. Thus, the paper extends the method of IRR so that it may help decision making in complicated cash-flow pattern observed in practice.

• Fashion & Textile Research Journal
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• v.9 no.4
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• pp.418-424
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• 2007

The purpose of this study is to propose a standard of converting 3D shape of men in twenties to 2D patterns. This can be a basis for scientific and automatic pattern making for high quality custom clothes. Firstly, representative 3D body shape of men was modeled. Then the 3D model was divided into 3 shells, front, side and back. Among them, the front shell was divided into 4 blocks by bust line and princess line. Secondly, curves are generated on each block according to matrix combination by grid method. Then triangles were developed into 2D pieces by reflecting the 3D curve length. The grid was arranged to maintain outer curve length. Next, the area of developed pieces and block were calculated and difference ratio between the block area and the developed pieces' area is calculated. Also, area difference ratio by the number of triangles is calculated. The difference ratio was represented as graphs and optimal section is selected by the shape of graphs. The optimal matrix was set considering connection with other blocks. Curves of torso upper front shell were regenerated by the optimal matrix and developed into pieces. We validated it's suitability by comparing difference ratio between the block area and the developed pieces' area of optimal section. The results showed that there was no significant difference between block area and the pieces' area developed by optimal matrix. The optimal matrix for 2D developing could be characterized as two types according to block's shape characteristics, one is affected by triangle number, the other is affected by number of raws more than columns. Through this study, both the 2D pattern developing from 3D body shape and 3D modeling from 2D pattern is possible, so it's standardization also possible.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.197-218
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• 1995

We first consider the random censorship model of survival analysis. Efron (1981) introduced two equivalent bootstrap methods for censored data. We propose a new bootstrap scheme, called Method 3, that acts conditionally on the censoring pattern when making inference about aspects of the unknown life-time distribution F. This article contains (a) a motivation for this refined bootstrap scheme ; (b) a proof that the bootstrapped Kaplan-Meier estimatro fo F formed by Method 3 has the same limiting distribution as the one by Efron's approach ; (c) description of and report on simulation studies assessing the small-sample performance of the Method 3 ; (d) an illustration on some Danish data. We also consider the model in which the survival times are censered by death times due to other caused and also by known fixed constants, and propose an appropriate bootstrap method for that model. This bootstrap method is a readily modified version of the Method 3.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.