• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.5
• /
• pp.1729-1736
• /
• 2015

Background: The purpose of this study was to investigate the associations among the internal health locus of control, depression, perceived health status, self efficacy, social support, and health-promoting behavior in Iranian breast cancer survivors and to determine influential variables. Materials and Methods: A predictive design was adopted. By convenient sampling the data of 262 breast cancer survivors in Iran were collected by questionnaires during 2014. Data were analyzed applying descriptive statistics, t-tests, one-way ANOVA, Pearson's correlation coefficients, and stepwise multiple regression. Results: The internal health locus of control, depression, perceived health status, self efficacy, social support and undergoing chemotherapy all correlated significantly with the health-promoting lifestyle. Stepwise multiple regression analysis revealed that social internal health locus of control, depression, perceived health status, self efficacy and social support and chemotherapy accounted for about 39.8% of the variance in health promoting lifestyle. The strongest influence was social support, followed by self efficacy, perceived health status, chemotherapy and depression. Conclusions: The results of the study clarifed the seriousness of social support, self efficacy, perceived health status and depression in determining the health-promoting lifestyle among Iranian breast cancer survivors. Health professionals should concentrate on these variables in designing plans to promoting a healthy lifestyle.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2004.05a
• /
• pp.160-163
• /
• 2004

In domestic, various repair materials and method systems to keep up with these reinforced concrete deteriorated due to salt damage, carbonation, chemical decay et. developed and applied. However, on-site quality control of various repair materials and method systems isn't achieved desirably because it is depend completely on a men of experience' opinions above all else regardless of various on-site environments. In this background, mock up test with due regard to real on-site environments was performed to secure fundamental data for establishment of desirable on-site quality control. Mock up test using repair mortar analyzed from angles of construction methods, mechanical spraying pressures, W/M. Construction methods were designed manpower method and spraying method, spraying pressures were designed 32, 42, 52 psi, W/M were designed 14.4, 15.4, $16.4\%$. And compressive strength, Chloride ion diffusion coefficient, bond strength, SEM. of mock up test specimens were evaluated. In conclusion, we confirmed excellency of mechanical spraying pressures, fined extremely excellency of condition of spraying pressure 42 ps, W/M $14.4\%$ within this study. therefore the results of this study will be useful to provide fundamental data for establishment of desirable on-site quality control.

• Journal of The Korean Association For Science Education
• /
• v.29 no.8
• /
• pp.848-860
• /
• 2009

This study examined the characteristics of scientists' writing on the laboratory reports written in the authentic open inquiry, and explored the possibility that the class discussion after the inquiries could influence the laboratory report writing. The samples were 131 10th graders in a science high school in Seoul. The control group (n=45) practiced traditional school science inquiries, the experimental group 1 (n=43) practiced the authentic open inquiries, and the experimental group 2 (n=43) practiced the authentic open inquiries and the class discussion after the laboratory activities. Their laboratory reports were analyzed into three parts - prediction (prediction with background and apposite description), data analysis (data transformation and critical analysis), and conclusion (objective description based on evidence). The frequency of the characteristics of scientist's writing in the experimental group was higher than the control group. Particularly, the differences of the prediction with background (p<.01) and the critical analysis of data (p<.05) were statistically significant. However, the frequency of writing the conclusion based on evidence was very low in all of the three groups. The result from comparing descriptions of reports showed that the writing prediction in experimental groups were more elaborate, and the data transformation in experimental groups were more correct, and the evaluation to data in experimental groups were more critical than the control group. And the descriptions of the critical evaluation to data and the finding flaw in methods were found in experimental groups 2, indicating that the class discussion can stimulate students' scientific thinking.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.11
• /
• pp.1089-1095
• /
• 2010

We propose an image segmentation method for auto-teaching system of PCB (Printed Circuit Board) assembly inspection machines. The inspection machine acquires images of all components in PCB, and then compares each image with its standard image to find the assembly errors such as misalignment, inverse polarity, and tombstone. The component window that is the area of component to be acquired by camera, is one of the teaching data for operating the inspection machines. To reduce the teaching time of the machine, we newly develop the image processing method to extract the component window automatically from the image of PCB. The proposed method segments the component window by excluding the soldering parts as well as board background. We binarize the input image by use of HSI color model because it is difficult to discriminate the RGB colors between components and backgrounds. The linear combination of the binarized images then enhances the component window from the background. By use of the horizontal and vertical projection of histogram, we finally obtain the component widow. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.8
• /
• pp.746-751
• /
• 2006

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.9-23
• /
• 2019

In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.

• Journal of the Ergonomics Society of Korea
• /
• v.30 no.1
• /
• pp.161-168
• /
• 2011

Objective: The aim of this study is to review the ISO(the International Organization for Standardization) standards recommendations on a human error tolerant control room design. Background: ISO TC(Technical Committee) 159 published a variety of international standards on design of mental and physical work, design of displays and controls, and workstation layout design. A proper edition of these standards can be a good resource for a human error tolerant control center design guidelines. Method: Recommendations of ISO TC 159 standards were grouped into arrangement of control suite, layout of control room, layout and dimensions of workstations, design of displays and controls, environmental design, alarm, automation, management system design, procedure and training. Results: It was found that some standards on the design of supervisory control and data acquisition(SCADA), alarm, automation, layout, workload management, and environment can be used for human error prevention guidelines in the control center design. Conclusion: ISO TC 159 standards were not sufficient to cover all the ergonomics area of control center design. Application: Designers can have technical aids from these ISO standards to improve ergonomic performance of their control center.

• Asian-Australasian Journal of Animal Sciences
• /
• v.16 no.4
• /
• pp.473-480
• /
• 2003

In order to control the genetic background noise in QTL mapping, cofactor markers were incorporated in single marker analysis (SMACO) and interval mapping (CIM). A simulation was performed to see how effective the cofactors were by the number of QTL, the number and the type of markers, and the marker spacing. The results of QTL mapping for the simulated data showed that the use of cofactors was slightly effective when detecting a single QTL. On the other hand, a considerable improvement was observed when dealing with more than one QTL. Genetic background noise was efficiently absorbed with linked markers rather than unlinked markers. Furthermore, the efficiency was different in QTL mapping depending on the type of linked markers. Well-chosen markers in both SMACO and CIM made the range of linkage position for a significant QTL narrow and the estimates of QTL effects accurate. Generally, 3 to 5 cofactors offered accurate results. Over-fitting was a problem with many regressor variables when the heritability was small. Various marker spacing from 4 to 20 cM did not change greatly the detection of multiple QTLs, but they were less efficient when the marker spacing exceeded 30 cM. Likelihood ratio increased with a large heritability, and the threshold heritability for QTL detection was between 0.30 and 0.05.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.1
• /
• pp.16-25
• /
• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.1.1-5
• /
• 1994

In the single sweep record of event-related potential (ERP), the peak latency of P300, which is one of the most prominent positive peaks in the ERP record, might fluctuate according to the recording conditions. The fluctuation of the peak latency (measurement fluctuation) is the summation of the fluctuation caused by physiological factor (physiological fluctuation) and one by noise of background EEG (noise fluctuation). We propsed a method for estimating the interval of the physiological fluctuation based on a limited number of single sweep records. The noise fluctuation was estimated by using the relationship between the signal-to-noise (SN) ratio and the noise fluctuation based on the P300 model and the background EEG model. The interval estimate of the physiological fluctuation were obtained by subtracting the interval estimate of the noise fluctuation from that of the measurement fluctuation. The proposed method was tested by using simulation data of ERP and applied to actual ERP and data of normal subjects, and gave satisfactory results.