• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• Women's Health Nursing
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• v.28 no.4
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• pp.329-337
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• 2022

Purpose: The purpose of this study was to investigate the influence of eHealth literacy, reproductive health knowledge, and self-esteem on early adult women's health-promoting behaviors (HPB). This study was based on Pender's health promotion model as a theoretical underpinning. Methods: Early adult women aged 18 to 35 years (n=165) were recruited by posting advertisements on social network sites for a student club and a faith-based community in Ansan, Korea. Willing individuals were invited to participate in the online survey from June 1 to June 30, 2022. Standardized instruments were used to measure HPB, eHealth literacy, reproductive health knowledge, and self-esteem. General characteristics included income level, perceived subjective health, and internet usage time. The collected data were analyzed using the independent t-test, one-way analysis of variance, Pearson correlation coefficients, and multiple regression. Results: The mean age of the respondents was 21.97±3.87 years. The total HPB score was 120.69, corresponding to a moderate level; and the total scores for eHealth literacy (30.24), knowledge of reproductive health (23.04), and self-esteem (35.62) were higher than the midpoint. The model explained 53.3% of variance in HPB, and self-esteem (β=.48, p<.001) was the most influential factor. Other influential factors were, in descending order, higher economic level, higher subjective health status, greater eHealth literacy, and less internet use time (<2 hours/day). Conclusion: In order to promote the health of early adult women, counseling or programs that positively improve self-esteem appear promising, and eHealth literacy should be considered as a way to promote HPB using information technology.

• The Journal of Advanced Prosthodontics
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• v.16 no.1
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• pp.57-65
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• 2024

PURPOSE. The aim of this stuldy was to compare the clinical marginal fit of CAD-CAM inlays obtained from intraoral digital impression or addition silicone impression techniques. MATERIALS AND METHODS. The study included 31 inlays for prosthodontics purposes of 31 patients: 15 based on intraoral digital impressions (DI group); and 16 based on a conventional impression technique (CI group). Inlays included occlusal and a non-occlusal surface. Inlays were milled in ceramic. The inlay-teeth interface was replicated by placing each inlay in its corresponding uncemented clinical preparation and taking interface impressions with silicone material from occlusal and free surfaces. Interface analysis was made using white light confocal microscopy (WLCM) (scanning area: 694 × 510 ㎛²) from the impression samples. The gap size and the inlay overextension were measured from the microscopy topographies. For analytical purposes (i.e., 95-%-confidence intervals calculations and P-value calculations), the procedure REGRESS in SUDAAN was used to account for clustering (i.e., multiple measurements). For p-value calculation, the log transformation of the dependent variables was used to normalize the distributions. RESULTS. Marginal fit values for occlusal and free surfaces were affected by the type of impression. There were no differences between surfaces (occlusal vs. free). Gap obtained for DI group was 164 ± 84 ㎛ and that for CI group was 209 ± 104 ㎛, and there were statistical differences between them (p = .041). Mean overextension values were 60 ± 59 ㎛ for DI group and 67 ± 73 ㎛ for CI group, and there were no differences between then (p = .553). CONCLUSION. Digital impression achieved inlays with higher clinical marginal fit and performed better than the conventional silicone materials.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.421-428
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• 2014

For the analysis of water supply network, demand-driven and pressure-driven analysis methods have been proposed. Of the two methods, demand-driven analysis (DDA) can only be used in a normal operation condition to evaluate hydraulic status of a pipe network. Under abnormal conditions, i.e., unexpected pipe destruction, or abnormal low pressure conditions, pressure-driven analysis (PDA) method should be used to estimate the suppliable flowrate at each node in a network. In order to carry out the pressure-driven analysis, head-outflow relationship (HOR), which estimates flowrate at a certain pressure at each node, should be first determined. Most previous studies empirically suggested that each node possesses its own characteristic head-outflow relationship, which, therefore, requires verification by using actual field data for proper application in PDA modeling. In this study, a model pipe network was constructed, and various operation scenarios of normal and abnormal conditions, which cannot be realized in real pipe networks, were established. Using the model network, data on pressure and flowrate at each node were obtained at each operation condition. Using the data obtained, previously proposed HOR equations were evaluated. In addition, head-outflow relationship at each node was analyzed especially under multiple pipe destruction events. By analyzing the experimental data obtained from the model network, it was found that flowrate reduction corresponding to a certain pressure drop (by pipe destruction at one or multiple points on the network) followed intrinsic head-outflow relationship of each node. By comparing the experimentally obtained head-outflow relationship with various HOR equations proposed by previous studies, the one proposed by Wagner et al. showed the best agreement with the exponential parameter, m of 3.0.

• The Journal of Engineering Geology
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• v.13 no.2
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• pp.207-225
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• 2003

According to the data analysis of the regional fracture systems in southern Korea, the fracture orientations show three dominant sets : NNE, NW and WNW. A NNE set is the most abundant and includes most of the largest fractures. The highest fracture density is shown in the Taebaegsan mineralized area corresponding to Ogchon nonmetamorphic belt and the lowest one in the southwestern area of southern Korea. In addition, the density is higher in nonmetamorphic sedimentary rocks such as Choseon Supergroup. Pyeongan Supergroup, Daedong Supergroup and Kyeongsang Supergroup than in Precambrian basements and Jurassic granites. The regional fractures in southern Korea can be classified into four orders designated $F_1,{\;}F_2,{\;}F_3{\;}and{\;}F_4${\;}and{\;}F_4$ on the basis of their trace length. It is quite significant that fractures of each order are self-similar with respect to orientation and the combined fracture length distribution indicates a power-law distribution with an exponent of -2.04. As fractures were analyzed based on the tectonic provinces, Gyeonggj Massif and Kyeongsang Basin have all orders of fractures from $F_1$ to $F_4$. Most of the large scale faults may be ascribed to the products of slip accumulation through multiple deformation. Others besides $F_1$ fractures are thought to be evenly distributed through the whole area of southern Korea.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.1-6
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• 2003

This paper analyses the performance of either the minimum variance distortionless response (MVDR) or the recursive least square (RLS) beamformer structured on the systolic array. Provided that the snapshot vector including the desired user's signal and the interferences with the noise is received at the array antenna. In order to improve the quality of received signal, MVDR or RLS algorithm can be utilized to update the beamformer weights recursively. Furthermore to increase the channel capacity, by the usage of the above schemes, the effect of the spatial filtering can be obtained which constructively combining multipath components corresponding to the desired user whereas the multiple access interferences (MAI) is nulled out on spatial domain. This paper introduces the MVDR and RLS beamformer structured on systolic array conducting the spatial filtering, and its performance under the multipath fading channel in the presence of multiple access interferences will be analyzed. To show the superior spatial filtering performances of the proposed scheme employing the systolic way structured beamformer, the computer simulations are carried out. And the validity of practical deployment of the proposed scheme will be confirmed throughout showing the BER behaviors and the beampatterns.

• Toxicological Research
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• v.30 no.1
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• pp.7-11
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• 2014

Malondialdehyde (MDA), used as an oxidative stress marker, is commonly assayed by measuring the thiobarbituric acid reactive substances (TBARS) using HPLC, as an indicator of the MDA concentration. Since the HPLC method, though highly specific, is time-consuming and expensive, usually it is not suitable for the rapid test in large-scale environmental epidemiologic surveys. The purpose of this study is to develop a simple and rapid method for estimating TBARS levels by using a multiple regression equation that includes TBARS levels measured with a microplate reader as an independent variable. Twelve hour urine samples were obtained from 715 subjects. The concentration of TBARS was measured at three different wavelengths (fluorescence: ${\lambda}-_{ex}$ 530 nm and ${\lambda}-_{em}$ 550 nm; ${\lambda}-_{ex}$ 515 nm and ${\lambda}-_{em}$ 553 nm; and absorbance: 532 nm) using microplate reader as well as HPLC. 500 samples were used to develop a regression equation, and the remaining 215 samples were used to evaluate the validity of the regression analysis. The induced multiple regression equation is as follows: TBARS level (${\mu}M$) = -0.282 + 1.830 ${\times}$ (TBARS level measured with a microplate reader at the fluorescence wavelengths ${\lambda}-_{ex}$ 530 nm and ${\lambda}-_{em}$ 550 nm, ${\mu}M$) -0.685 ${\times}$ (TBARS level measured with a microplate reader at the fluorescence wavelengths ${\lambda}-_{ex}$ 515 nm and ${\lambda}-_{em}$ 553 nm, ${\mu}M$) + 0.035 ${\times}$ (TBARS level measured with a microplate reader at the absorbance wavelength 532 nm, ${\mu}M$). The estimated TBARS levels showed a better correlation with, and are closer to, the corresponding TBARS levels measured by HPLC compared to the values obtained by the microplate method. The TBARS estimation method reported here is simple and rapid, and that is generally in concordance with HPLC measurements. This method might be a useful tool for monitoring of urinary TBARS level in environmental epidemiologic surveys with large sample sizes.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.291-299
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• 2016

Recently, the knowledge industry center has played a role as a facility that creates economic added value because of the high-tech companies related to the knowledge industry, information, and communication. On the other hand, support facilities that are provided in the knowledge industry center are meant to support the company in the center and improve the working conditions of laborers. On the other hand, the support policy established by the government applies to some companies, and none of the support policy is carried out in supporting facilities in the knowledge industry center. In this study, multiple analysis was performed, focusing on the support facilities in the knowledge industry center that aims to improve the working environment of laborers. This study suggests the introduction of guidelines to secure adequate area, depending on the type of supporting facilities in the Knowledge Industrial Center. The sharing of facilities, such as cultural and commercial use for Knowledge Industrial Center, corresponding to poor provision, is recommended. Because the analysis of IPA indicates that the area of commercial support facilities are higher than others and cultural facilities are more important and preferred, it is necessary to compose support facilities that consider the user's individual characteristics. Facilities impacting the working environment need to be planned carefully through a district unit plan at the initial stages of development, thus assisting the production activity of workers.