• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• The Research Journal of the Costume Culture
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• v.12 no.5
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• pp.743-756
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• 2004

The purpose of this study is to set measurement parts which are important to identify the size and shape of the head in order to produce tightly fitted hats, and to identify detailed sizes and the major factors of head shape classifications of Korean women. A total of 285 Korean women's in the age group of 18 to 35 years old. It were measured through the direct measurement method by selecting 67 measurement items. Materials were analyzed by SPSS Ver.10 and technology statistics and factor analysis were performed according to the agenda. An attempt was made to conduct factor analysis of the measured region of the head in women's. Here, this study drew the head and the facial parts horizontal size as Factor 1, the head and facial parts vertical size as Factor 2, the circumference and width item as Factor 3, the factor representing the form of head height as Factor 4, the factor expressing the proportion of the facial form as Factor 5 and the factor about the frontal and back head form on the plan of the middle as Factor 6. Through this study, we will be able to systemize head measurement materials that can differentiate Korean's head from other peoples' and can use the results in developing head shape models according to Korean's head shape by selecting major head parts needed to identify the sizes and shapes.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.61-74
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• 2009

This research reviewed previous research related to Performance Measurement Models of Knowledge Management (PMMKM) in order to integrate their findings with more recent research and construct a new PMMKM. This new hypothetical PMMKM consists of an input sector, a process sector, an outcome sector, and an infrastructure sector. Each sector has three measurement items with the exception of the infrastructure sector which has two. Empirical analyses testing the overall performance model validity of the hypothetical PMMKM were favorable. However, it show be noted that the "share process" and "utilization process" items in the process sector merged into one single item. The same is true with the "individual outcome" and "organization outcome" items in the outcome sector found one single item. The study's results reveal three implications with respect to performance. First, there are derived integrated performance measurement sectors and items based on overall management process of knowledge management, which can be practically applied to the government related research entities. This became apparent after extensive review or previous theoretical studies related to the public sector and private sector. Second, weighted performance measurement of knowledge management using AHP (Analytic Hierarchy Process) Analysis makes it possible to propose PMMKM in government sponsored research institutes. Finally, measuring performance to management knowledge, as shown in this study, will prove useful for inside and outside experts who propose specific guidelines and methodologies for Knowledge management at government sponsored research institutes.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.12-17
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• 2010

The oil tanker ship over 150GRT must equip oil content meter which satisfy requirements of revised MARPOL 73/78. Online measurement of oil content in complex samples is required to have fast response, continuous measurement, and satisfaction of ${\pm}10ppm$ or ${\pm}10%$ error in this field. The research of this paper is to develop oil content measurement system using analysis of light transmission and scattering among turbidity measurement methods. Light transmission and scattering are analytical methods commonly used in instrumentation for online turbidity measurement of oil in water. Gasoline is experimented as a sample and the oil content approximately ranged from 14ppm to 600ppm. TSK Fuzzy Model may be suitable to associate variously derived spectral signals with specific content of oil having various interfering factors. Proposed Parallel TSK Fuzzy Model is reasonably used to classify oil content in comparison with other models. Those measurement methods would be effectively applied and commercialized to oil content meter that is key components of oil discharge monitoring control equipment.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.6 s.336
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• pp.53-60
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• 2005

Considerable efforts has been recently devoted to the determination of accurate channel models for the power line environment, both for the indoor and outdoor cases. The common limitation of the known and previously published models is the particular type of approach followed. This paper is concerned with a power line channel characteristic measurement for the more fast and efficiently power line communication experiment. The need arises from the fact that indoor power cables consist of conductors and inductors. A capacitive load simulator is a essential equipment in the power line modem development for indoor power line network. We accomplished a channel data base by the frequency response method about the total 224 capacitor load cases. On the basis of this measurement modeling it is here revealed that the power line communication channel is a more deterministic media.

• The Research Journal of the Costume Culture
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• v.25 no.4
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• pp.476-487
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• 2017

The purpose of this study was to suggest torso patterns that fit the three main body shapes of elderly obese women. To reduce time, costs, and also the trial and error needed to make patterns, the CLO program for 3D test wear was employed. Three virtual models for aged obese women were use, with the YUKA system used to produce torso patterns. 3D simulation of test wear and corrections was done to design optimal torso patterns. The results were as follows: First, for the three models of obese women's body shapes as realized by CLO 3D, Type 1 is lower-body obesity shapes, Type 2 is abdominal obesity shapes, and Type 3 is whole-body obesity shapes. Second, to design the study patterns, actual measurement values, back waist length and waist to hip length, were used. The armhole depth (B/4-1.5), front interscye (B/6+2.3), front neck width (B/12-0.5), front neck depth (B/12+0.5), front waist measurement (W/4+ 1.5+D), front hip measurement (H/4+2+0.5), and back hip measurement (H/4+3-0.5) were calculated using formulas. Third, according to the results of test-wearing the study patterns, reduced front neck width and depth improved the neck fit and reduced armhole depth bettered loose or plunging armhole girth and also reduced the sagging of bust c.. Also, tight sidesfrom aprotruded waist and abdomen improved with the increase of surpluses in the back waist and also back and front hip c. The exterior was enhanced by displacement of back and front darts, which distributed surpluses better.

• Smart Structures and Systems
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• v.25 no.2
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• pp.135-153
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• 2020

There has been a sustained interest towards the non-contact structural displacement measurement by means of videogrammetric technique. On the way forward, one of the major concerns is the spurious image drift induced by temperature variation. This study therefore carries out an investigation into the temperature effect of videogrammetric technique, focusing on the exploration of the mechanism behind the temperature effect and the elimination of the temperature-caused measurement error. 2D videogrammetric measurement tests under monotonic or cyclic temperature variation are first performed. Features of measurement error and the casual relationship between temperature variation and measurement error are then studied. The variation of the temperature of digital camera is identified as the main cause of measurement error. An excellent linear relationship between them is revealed. After that, camera parameters are extracted from the mapping between world coordinates and pixels coordinates of the calibration targets. The coordinates of principle point and focal lengths show variations well correlated with temperature variation. The measurement error is thought to be an outcome mainly attributed to the variation of the coordinates of principle point. An approach for eliminating temperature-caused measurement error is finally proposed. Correlation models between camera parameters and temperature are formulated. Thereby, camera parameters under different temperature conditions can be predicted and the camera projective matrix can be updated accordingly. By reconstructing the world coordinates with the updated camera projective matrix, the temperature-caused measurement error is eliminated. A satisfactory performance has been achieved by the proposed approach in eliminating the temperature-caused measurement error.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.85-94
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• 2013

This paper presents a novel mobile measurement system with multi antennas which enable mobile measurement as well as fixed measurement with telescope mast. Proposed system installed 4 omni directional antennas for the space diversity process and one directional log periodic antenna for the simultaneous conventional fixed measurement. Whole antenna systems are connected to the custom DTV channel analyzers with Ethernet networks respectively and processed by the main controller to calculate real time average receive levels. To prove the performance of proposed system, the typical receive models are categorized as 3 area types - open area, building area and house area, and then intensive field tests were performed through mobile and fixed measurement phases. With these measurement data, the relationships between mobile and fixed measurement are analyzed, and the concept of compensation factor is proposed to assume the average receive level of signal. The field test is fulfilled as a co-work with public broadcasters and the proposed system is applied to the intensive coverage measurement projects for metropolitan areas by the korean government agencies.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.