• Women's Health Nursing
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• v.29 no.4
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• pp.302-316
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• 2023

Purpose: This study aimed to develop and validate a structural model for the quality of life (QoL) among high-risk pregnant women, based on Roy's adaptation model. Methods: This cross-sectional study collected data from 333 first-time mothers diagnosed with a high-risk pregnancy in two obstetrics and gynecology clinics in Cheonan, Korea, or participating in an online community, between October 20, 2021 and February 20, 2022. Structured questionnaires measured QoL, contextual stimuli (uncertainty), coping (adaptive or maladaptive), and adaptation mode (fatigue, state anxiety, antenatal depression, maternal identity, and marital adjustment). Results: The mean age of the respondents was 35.29±3.72 years, ranging from 26 to 45 years. The most common high-risk pregnancy diagnosis was gestational diabetes (26.1%). followed by preterm labor (21.6%). QoL was higher than average (18.63±3.80). Above-moderate mean scores were obtained for all domains (psychological/baby, 19.03; socioeconomic, 19.00; relational/spouse-partner, 20.99; relational/family-friends, 19.18; and health and functioning, 16.18). The final model explained 51% of variance in QoL in high-risk pregnant women, with acceptable overall model fit. Adaptation mode (β=-.81, p=.034) and maladaptive coping (β=.46 p=.043) directly affected QoL, and uncertainty (β=-. 21, p=.004), adaptive coping (β=.36 p=.026), and maladaptive coping (β=-.56 p=.023) indirectly affected QoL. Conclusion: It is essential to develop nursing interventions aimed at enhancing appropriate coping strategies to improve QoL in high-risk pregnant women. By reinforcing adaptive coping strategies and mitigating maladaptive coping, these interventions can contribute to better maternal and fetal outcomes and improve the overall well-being of high-risk pregnant women.

• Earthquakes and Structures
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• v.27 no.2
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• pp.127-142
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• 2024

The amount of wave propagation through a rubber concrete construction is the subject of the current investigation. Rubber tire waste was used to make two different types of cement mixtures. One type contains sand substitute in amounts ranging from 15% to 60% of the total volume, while the other has gravel with diameters of 3/8 and 8/15 and 15% sand in the same mixture. A wide variety of concrete forms and compositions were created, and their viscous and solid state characteristics were assessed, along with their short-, medium-, and long-term strengths. Diffusion, density, mechanical strength resistance to compressive force, and ultrasound wave propagation were also assessed. The water-to-cement ratio and plasticizer were used in this investigation. In the second part of the study, an analytical model is presented that simulates the experimental model in predicting the speed of waves and the frequencies accompanying them for this type of mixture. Higher order shear deformation beam theory for wave propagation in the rubberized concrete beam is developed, considering the bidirectional distribution, which is primarily expressed by the density, the Poisson coefficient, and Young's modulus. Hamilton's concept is used to determine the governing equations of the wave propagation in the rubberized concrete beam structure. When the analytical and experimental results for rubber concrete beams were compared, the outcomes were very comparable. The addition of rubber gravel and sandy rubber to the mixture both resulted in a discernible drop in velocities and frequencies, according to the data.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3866-3873
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• 2000

In this paper, we investigates the long-term memory and the Correct answer rate of the foreign exchange data (Yen/Dollar) that is one of economic time series, There are many cases where two kinds of fractal dimensions exist in time series generated from dynamical systems such as AR models that are typical models having a short terrr memory, The sample interval separating from these two dimensions are denoted by kcrossover. Let the fractal dimension be $D_1$ in K < $k^{crossover}$,and $D_2$ in K > $k^{crossover}$ from the statistics mode. In usual, Statistic models have dimensions D1 and D2 such that $D_1$ < $D_2$ and $D_2\cong2$ But it showed a result contrary to this in the real time series such as NIKKEL The exchange data that is one of real time series have relation of $D_1$ > $D_2$ When the interval between data increases, the correlation between data increases, which is quite a peculiar phenomenon, We predict exchange data by neural networks, We confirm that $\beta$ obrained from prediction errors and D calculated from time series data precisely satisfy the relationship $\beta$ = 2-2D which is provided from a non-linear model having fractal dimension, And We identified that the difference of fractal dimension appeaed in the Correct answer rate.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.319-326
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• 2004

To help network intrusion detection systems(NIDSs) keep up with the demands of today's networks, that we the increasing network throughput and amount of attacks, a radical new approach in hardware and software system architecture is required. In this paper, we propose a Network Processor(NP) based In-Line mode NIDS that supports the packet payload inspection detecting the malicious behaviors, as well as the packet filtering and the traffic metering. In particular, we separate the filtering and metering functions from the deep packet inspection function using two-level searching scheme, thus the complicated and time-consuming operation of the deep packet inspection function does not hinder or flop the basic operations of the In-line mode system. From a proto-type NP-based NIDS implemented at a PC platform with an x86 processor running Linux, two Gigabit Ethernet ports, and 2.5Gbps Agere PayloadPlus(APP) NP solution, the experiment results show that our proposed scheme can reliably filter and meter the full traffic of two gigabit ports at the first level even though it can inspect the packet payload up to 320 Mbps in real-time at the second level, which can be compared to the performance of general-purpose processor based Inspection. However, the simulation results show that the deep packet searching is also possible up to 2Gbps in wire speed when we adopt 10Gbps APP solution.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.68-75
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• 2011

A vehicle fuel economy is very important issue in view of fuel cost and environmental regulation. It has been improved according to the performance improvement of the vehicle engine, power train and many components. It was evaluated at given mode (LA-4, FTP-75, etc) on an engine dynamometer or computer simulation program. In this paper, the fuel economy improvement cruise control algorithms as controling a vehicle velocity by road load calculated and predicted in a real expressway with gradient was studied. Firstly, the altitude and distance data which was measured with GPS sensor was already installed in the ECU of a vehicle. Then the vehicle equipped with GPS receiver is driven the same expressway. The ECU calculates the gradient angle and the in-/decreasing velocity using the gradient angle by comparing the current received distance and altitude data from GPS with the saved data ahead of the vehicle. Therefore the ECU can calculate and predict the vehicle velocity considering tolerance velocity of next position with running. Then the ECU controls the vehicle velocity to meet this predicted velocity in all section. Three cruise control algorithms with the different velocity profiles for the improvement of fuel economy are proposed and compared with the computer simulation results that the vehicle runs on Youngdong expressway. The proposed CVELCONT2 and CVELCONT3 algorithms were improved 3.7% and 4.8% of fuel economy compared with CONSTVEL which is steady cruising algorithm. These two algorithms are recommended as the Eco-cruise drive methodologies in this paper.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.1-7
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• 2018

This paper is analyzed for the thermal characteristics(1 year) of the 6 components(DC breaker, DC filter(including capacitor and discharge resistance), IGBT(Insulated gate bipolar mode transistor), AC filter, AC breaker, etc.) of a photovoltaic power generation-based PCS(Power conditioning system) below 20 kW. Among the modules, the discharge resistance included in the DC filter indicated the highest heat at $125^{\circ}C$, and such heat resulting from the discharge resistance had an influence on the IGBT installed on the rear side the board. Therefore, risk priority through risk priority number(RPN) of FMEA(Failure modes and effects analysis) sheet is conducted for classification into top 10 %. According to thermal characteristics and FMEA, it is necessary to pay attention to not only the in-house defects found in the IGBT, but also the conductive heat caused by the discharge resistance. Since it is possible that animal, dust and others can be accumulated within the PCS, it is possible that the heat resulting from the discharge resistance may cause fire. Accordingly, there are two options that can be used: installing a heat sink while designing the discharge resistance, and designing the discharge resistance in a structure capable of avoiding heat conduction through setting a separation distance between discharge resistance and IGBT. This data can be used as the data for conducting a comparative analysis of abnormal signals in the process of developing a safety device for solar electricity-based photovoltaic power generation systems, as the data for examining the fire accidents caused by each module, and as the field data for setting component management priorities.

• Asia Marketing Journal
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• v.12 no.2
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• pp.83-110
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• 2010

Dissatisfied customers may express their dissatisfaction behaviorally. These behavioral responses may impact the firms' profitability. How do we model the impact of self regulatory orientation on emotions and subsequent customer behaviors? Obviously, the positive and negative emotions experienced in these situations will influence the overall degree of satisfaction or dissatisfaction with the service(Zeelenberg and Pieters 1999). Most likely, these specific emotions will also partly determine the subsequent behavior in relation to the service and service provider, such as the likelihood of complaining, the degree to which customers will switch or repurchase, and the extent of word of mouth communication they will engage in(Zeelenberg and Pieters 2004). This study investigates the antecedents, consequences of negative consumption emotion and the moderate effect of attribution processing in an integrated model(self regulatory mode → specific emotions → behavioral responses). We focused on the fact that regret and disappointment have effects on consumer behavior. Especially, There are essentially two approaches in this research: the valence based approach and the specific emotions approach. The authors indicate theoretically and show empirically that it matters to distinguish these approaches in services research. and The present studies examined the influence of two regulatory mode concerns(Locomotion orientation and Assessment orientation) with making comparisons on experiencing post decisional regret and disappointment(Pierro, Kruglanski, and Higgins 2006; Pierro et al. 2008). When contemplating a decision with a negative outcome, it was predicted that high (vs low) locomotion would induce more disappointment than regret, whereas high (vs low) assessment would induce more regret than disappointment. The validity of the measurement scales was also confirmed by evaluations provided by the participating respondents and an independent advisory panel; samples provided recommendations throughout the primary, exploratory phases of the study. The resulting goodness of fit statistics were RMR or RMSEA of 0.05, GFI and AGFI greater than 0.9, and a chi-square with a 175.11. The indicators of the each constructs were very good measures of variables and had high convergent validity as evidenced by the reliability with a more than 0.9. Some items were deleted leaving those that reflected the cognitive dimension of importance rather than the dimension. The indicators were very good measures and had convergent validity as evidenced by the reliability of 0.9. These results for all constructs indicate the measurement fits the sample data well and is adequate for use. The scale for each factor was set by fixing the factor loading to one of its indicator variables and then applying the maximum likelihood estimation method. The results of the analysis showed that directions of the effects in the model are ultimately supported by the theory underpinning the causal linkages of the model. This research proposed 6 hypotheses on 6 latent variables and tested through structural equation modeling. 6 alternative measurements were compared through statistical significance test of the paths of research model and the overall fitting level of structural equation model and the result was successful. Also, Locomotion orientation more positively influences disappointment when internal attribution is high than low and Assessment orientation more positively influences regret when external attribution is high than low. In sum, The results of our studies suggest that assessment and locomotion concerns, both as chronic individual predispositions and as situationally induced states, influence the amount of people's experienced regret and disappointment. These findings contribute to our understanding of regulatory mode, regret, and disappointment. In previous studies of regulatory mode, relatively little attention has been paid to the post actional evaluative phase of self regulation. The present findings indicate that assessment concerns and locomotion concerns are clearly distinct in this phase, with individuals higher in assessment delving more into possible alternatives to past actions and individuals higher in locomotion engaging less in such reflective thought. What this suggests is that, separate from decreasing the amount of counterfactual thinking per se, individuals with locomotion concerns want to move on, to get on with it. Regret is about the past and not the future. Thus, individuals with locomotion concerns are less likely to experience regret. The results supported our predictions. We discuss the implications of these findings for the nature of regret and disappointment from the perspective of their relation to regulatory mode. Also, self regulatory mode and the specific emotions(disappointment and regret) were assessed and their influence on customers' behavioral responses(inaction, word of mouth) was examined, using a sample of 275 customers. It was found that emotions have a direct impact on behavior over and above the effects of negative emotions and customer behavior. Hence, We argue against incorporating emotions such as regret and disappointment into a specific response measure and in favor of a specific emotions approach on self regulation. Implications for services marketing practice and theory are discussed.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.936-946
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• 2009

A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.