• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.81-88
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• 2021

Structural health monitoring (SHM) systems have attracted considerable interest owing to the frequent earthquakes over the last decade. Smart concrete is a technology that can analyze the state of structures based on their electro-mechanical behavior. On the other hand, most research on the self-sensing response of smart concrete generally investigated the electro-mechanical behavior of smart concrete under a static loading rate, even though the loading rate under an earthquake would be much faster than the static rate. Thus, this study evaluated the electro-mechanical behavior of smart ultra-high-performance concrete (S-UHPC) at three different loading rates (1, 4, and 8 mm/min) using a Universal Testing Machine (UTM). The stress-sensitive coefficient (SC) at the maximum compressive strength of S-UHPC was -0.140 %/MPa based on a loading rate of 1 mm/min but decreased by 42.8% and 72.7% as the loading rate was increased to 4 and 8 mm/min, respectively. Although the sensing capability of S-UHPC decreased with increased load speed due to the reduced deformation of conductive materials and increased microcrack, it was available for SHM systems for earthquake detection in structures.

• Korean Journal of School Psychology
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• v.17 no.1
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• pp.17-38
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• 2020

This study examined the factor structure of the Child Behavior Checklist Dysregulation Profile(CBCL-DP) for school-aged children in Korea identified differences in the level of maladjustment and problematic behaviors between the clinical group which had characteristics of CBCL-DP and the control group which did not. Confirmative factor analysis was performed on three alternative models from the literature to determine which was the most appropriate factor structure for the CBCL-DP. The result showed that the bi-factor model fit the sample data better than both the one and second-factor models. To confirm that the bi-factor model was the most appropriate factor structure, regression paths with relevant variables examined. The showed that CBCL-DP with the bi-factor model was associated with executive function difficulty as reported by parents and with school adjustment and all sub-factors of strength and difficulty as reported by teachers. The results also showed that this model had a different relationship with anxiety/depression, aggressive behavior, and attention problems than the other models. The clinical group was shown to have more executive function difficulty, worse adjustment of school life and to be less likely to engage in desired behaviors than the control group. These results indicate the CBCL-DP is more related to negative outcomes than any other factor, and that the bi-factor model was found to best fit the sample data, consistent with other studies. The early discovery of CBCL-DP can be used to provide interventions for high-risk children who exhibit emotional and behavioral problems, making its detection a significant diagnostic tool. The implications of these result, the limitations of this study, and areas for future research are discussed in this paper.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Korean Journal of Health Education and Promotion
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• v.24 no.5
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• pp.23-37
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• 2007

Background: Even though cervical cancer poses a significant global cancer burden, successful implementations for early detection offer an opportunity to prevent deaths and reduce the cancer burden. In Korea, cervical cancer is the most prevalent type of cancer among adult women, but it is one of the few cancers in which a consensus-approved screening test exists for early diagnosis, Pap test, that can be combined with highly efficacious treatment regimens for early-stage disease. Purpose: This study was carried out to identify the cognitive-behavioral factors associated with cervical cancer screening behavior among adult women, aged 40 to 59, and to develop tailored messages and to evaluate the effectiveness of stage-matched educational program. Method: A total of 283 women who aged 40 years or older was recruited in Seoul, from September, 1st to November, 14th, 2003. The intervention group (N=162) and the control group (N=121) were selected from five sub-districts in Seocho-gu, Seoul. Building on the TTM, a quasi-experimental study was conducted to test the effectiveness of stages-matched intervention addressed at the five stages of cervical cancer screening behavior. Women in the intervention group were randomly assigned to one of two conditions, internet or postal services. Results: In our results, 88.9% of participants had received a Pap test at least once in their life-time, and 65.4% had got it in the past two years. With regard to cognitive-behavioral factors, the stages-matched educational program increased attitude and process of change for cervical cancer screening. The percentage changed was the largest in maintenance stage. With regard to delivery methods for tailored messages, the print materials were more effective at increasing screening adherence than the e-mail. Whereas the postal service group showed remarkable the change of behavior stage, the internet service group did not. Also it was not shown any difference of the satisfaction with stages-matched educational program between internet and postal service groups. Conclusion: This study suggested that cervical cancer screening behavior could be changed by tailored messages which had developed with cognitive-behavioral factors. The stages-matched educational program was effective to promote the screening adherence for cervical cancer.

• Journal of Embryo Transfer
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• v.22 no.3
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• pp.185-190
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• 2007

The objective of this study was to investigate the characteristics of various estrous behavior and ovulation time in dairy cows and heifers. In total, 73 ovulations and 61 estrous detection were observed in 89 Holstein cows. Various estrous behavior were observed during 72 hours from two days after $PGF_2{\alpha}$ injection and their relation with the time of ovulation(ultrasound examinations at 4-h intervals) was investigated. In estrous periods, the rate of sniffing, chin resting, mounting and standing heat was 81%, 78%, 78% and 56%, respectively in cows. In heifers, the rate of sniffing, chin resting, mounting and standing heat was 61%, 68%, 82% and 76%, respectively. Ovulation in cows and heifers occurred $25.58{\pm}7.94\;and\;25.55{\pm}5.72h$ after onset of estrus, and $13.42{\pm}7.14\;and\;7.48{\pm}7.41h$ after end of estrus, respectively. Interval between onset of estrus and ovulation time was significantly (p<0.05) shorter for standing heat ($17.33{\pm}5.83\;h$) than for mounting, sniffing and chin resting ($23.58{\pm}5.12\;h,\;24.25{\pm}6.09\;h,\;23.42{\pm}6.04\;h$) in cows but not significantly different in heifers. Interval between end of standing heat and ovulation time was significantly (p<0.05) shorter for heifer($6.38{\pm}4.80$) than for cows($13.05{\pm}4.53$). Our results show that characteristics of estrous behavior and ovulation in dairy heifers are different to that of cows.

• Journal of Embryo Transfer
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• v.22 no.1
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• pp.9-13
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• 2007

The objective of this study was to investigate the relationship between various estrous behavior and ovulation time, and to determine which estrous behavior could predict ovulation time more accurately. In total, 37 ovulations and 28 estrous detection were observed in 51 Holstein-Friesian dairy cows. Various estrous behavior were observed during 72 h from two days after $PGF_2{\alpha}$ injection and their relation with the time of ovulation (ultrasound examinations at 4-h intervals) was investigated. In estrous periods, the rate of sniffing, chin resting, mounting, standing heat was 81%, 78%, 78% and 56%, respectively. Ovulation occurred $25.6{\pm}7.9h$ after onset of estrus (ranging between 7 and 37h) and $13.4{\pm}7.1h$ after end of estrus (ranging between 1 and 28h). Interval between onset of estrus and ovulation time was significantly (p<0.05) shorter for standing heat $(17.33{\pm}5.83h)$ than for mounting, sniffing and chin resting $(23.58{\pm}5.12h,\;24.25{\pm}6.09h,\;23.42{\pm}6.04h)$. In 88% of the animals that displayed mounting, ovulation occurred between $16{\sim}28h$ after onset of mounting. Onset of standing heat, sniffing and chin resting occurred between $10{\sim}22(81%)h,\;16{\sim}28(79%)h\;and\; 19{\sim}31(79%)h$ before ovulation respectively. Sniffing and chin resting were displayed during the non-estrous period and are therefore, not useful predictors of ovulation time. The standing heat and mounting can be a good predictor for time of ovulation but the disadvantage of using standing heat is that only a limited number of cows display standing heat. Thus, it is concluded that mounting behavior could be the best predictor for time of ovulation.

• The Journal of Engineering Geology
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• v.7 no.2
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• pp.139-149
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• 1997

Electrical resistivity monitoring methods were adopted to detect groundwater table change in alluvium. Numerical modelling test using finite element method(FEM) and field resisfivity monitoring were conducted in the study. The field monitoring data were acquired in the alluvium deposit site in Jeong-Dong Ri, Geum River where pumping test had been conducted continuously for 20 days to make artificial changes of groundwater table. The unit distance of the electrode array was 4m and 21 fixed electrodes were applied in numerical calculation and field data acquisition. "Modified Wenner" and dipole-dipole array configurations were used in the study. The models used in two-dimensional numerical test were designed on the basis of the simplifving geological model of the alluvium in Jeong Dong Ri, Geum River. Numerical test results show that the apparent resistivity pseudosections were changed in the vicinity of the pootion where groundwater table was changed. Furthermore, there are some apparent resistivity changes in the boundary between aquifer and crystalline basement rock which overlays the aquifer. The field monitoring data also give similar results which were observed in numerical tests. From the numerical test using FEM and field resistivity monitoring observations in alluvium site of Geum River, the electrical monitoring method is proved to be a useful tool for detecting groundwater behavior including groundwater table change. There are some limitations, however, in the application of the resistivity method only because the change of groundwater table does not give enough variations in the apparent resistivity pseudosections to estimate the amount of groundwater table change. For the improved detection of groundwater table changes, it is desirable to combine the resistivity method with other geophysical methods that reveal the underground image such as high-resolution seismic and/or ground penetrating radar surveys.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.473-479
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• 2016

We report a highly selective and sensitive 200 MHz Surface Acoustic Wave (SAW) sensor that can detect cyanide ion in aqueous solution using surface immobilized thioester molecules in combination with gold nanoparticles (AuNPs). To construct the sensor device, a monolayer of thioester compound was immobilized on the SAW sensor surface. At the sensor surface, hydrolysis of thioester group by nucleophilic addition of cyanide occurred and the resulting free thiol unit bound to AuNP to form thiol-AuNP conjugate. For the signal enhancement, gold staining signal amplification process was introduced subsequently with gold (III) chloride trihydrate and reducing agent, hydroxylamine hydrochloride. The SAW sensor showed a detection ability of $17.7{\mu}M$ for cyanide in aqueous solution and demonstrated a saturation behavior between the frequency shift and the concentration of cyanide ion. On the other hand, our SAW sensor had no activities for other anions such as fluoride ion, acetate ion and sulfate ion, moreover, no significant interference observed by other anions. Finally, all the experiments were carried out in-house developed sensor and fluidics modules to obtain highly reproducible results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.190-197
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• 2000

Anions such as C $l^{[-10]}$ , N $O_3$$^{[-10]}$ , S $O_4$$^{2-}$, P $O_4$$^{3-}$, and organic anions, that do not become a part of the clay mineral crystal lattice, are of considerable interest in soils which are a potential sink caused by acid rain. In this paper, elution of native sulfate and breakthrough curves (BTC) were obtained from miscible displacement of non-specifically or specifically adsorbed anions through non-saturated or saturated Bt soil of Chungwon series. The shape and position of the BTC's could be affected by adsoprtion and ion exchange onto the soil particle surfaces. Measured BTC's for oxalic acid under unsaturated and saturated conditions showed that less pore volumes were required to displace the native S $O_4$$^{2-}$S from the soil column, and that maximum detection limit of oxalic acid reached earlier than under unsaturated. The retarded BTC's to the righthand side could be attributed by different adsorption behavior of each anion, although BTC's may be influenced by the smaller order of velocity change. The alternate breakthrough and elution curves show the rapid approach to the maximum detection limit of C/Co = 1, compared to progressive tailing of elution curve to reach to C/Co = 0. The probable explanation for asymmetric elution patterns for both anion is that the anion was selectively adsorbed on the positively charged soil surface from the solution passing in the soil column. On the other hand, the variations of pH in effluent showed that pH was increased to 7 in the first 6 pore volume and then gradually decreased to pH 4.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.178-188
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• 2019

When using a distributed system, it is very important to know the intention of a target ship in order to prevent collisions. The action taken by a certain ship for collision avoidance and the action of the target ship it intends to avoid influence each other. However, it is difficult to establish a collision avoidance plan in consideration of multiple-ship situations for this reason. To solve this problem, a Distributed Stochastic Search Algorithm (DSSA) has been proposed. A DSSA searches for a course that can most reduce cost through repeated information exchange with target ships, and then indicates whether the current course should be maintained or a new course should be chosen according to probability and constraints. However, it has not been proven how the parameters used in DSSA affect collision avoidance actions. Therefore, in this paper, I have investigated the effect of the parameters and weight factors of DSSA. Experiments were conducted by combining parameters (time window, safe domain, detection range) and weight factors for encounters of two ships in head-on, crossing, and overtaking situations. A total of 24,000 experiments were conducted: 8,000 iterations for each situation. As a result, no collision occurred in any experiment conducted using DSSA. Costs have been shown to increase if a ship gives a large weight to its destination, i.e., takes selfish behavior. The more lasting the expected position of the target ship, the smaller the sailing distance and the number of message exchanges. The larger the detection range, the safer the interaction.