• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.4
• /
• pp.2247-2254
• /
• 2016

Background: The incidence of breast cancer is rising in Oman, and the disease is diagnosed at late stages, when treatment success is limited. Omani women might benefit from better awareness, so that breast cancer can be detected early and treated. This study was conducted to assess Omani women's levels of breast cancer awareness and early detection practice, and explore factors which might influence these levels. Materials and Methods: A mixed methods study was conducted in 2014, including a quantitative survey of 1,372 and a qualitative assessment of 19 Omani women, aged ${\geq}20years$ from five Omani governorates using convenient sampling. Demographic information and scores for awareness levels were used in a multivariate regression model to investigate factors associated with awareness. Thematic analysis and interpretive description were used to analyse the qualitative data. Results: The overall means for early detection and general awareness scores were 0.58 (SD 0.24) and 0.46 (SD 0.21), respectively. General awareness was significantly associated with age, education, income and familiarity with cancer patients (p<0.05), while early detection was significantly associated with age, marital status and education. A majority of women (59.5%) agreed with a belief in 'evil eye' or envy as a risk factor for breast cancer. Women discussed various factors which may empower or inhibit awareness, including the cultural-religion-fatalistic system, personal-familial-environmental system, and healthcare-political-social system. Conclusions: The overall low scores for awareness and early detection, and the survey of local beliefs highlight a severe necessity for a contextually-tailored breast cancer awareness intervention programme in Oman.

• Journal of Korea Water Resources Association
• /
• v.45 no.12
• /
• pp.1227-1241
• /
• 2012

In this study, a novel water surface detection method was proposed. In the method water surface is detected by analysis on correlation coefficients obtained from rectangular control areas of the same vertical position in two successive images including both water surface and staff gauge. Four methods respectively based on threshold, peak, slope and variance ratio, are used to identify water surface from vertical distribution of correlation coefficient. In addition, swaying correction algorithm and statistical filtering are applied to minimize outliers caused by positional image mismatch. Images taken from 28 different sites during low flow were tested to evaluate the method. Mean relative error to eye measurement was approximately from 3.4 to 5.7 cm. As long as water surface moves, this method can be used to improve image stage gauge by supplementing the previous water surface detection method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.638-642
• /
• 2002

User in video data utilization of high-capacity can grasp whole video data at a look. Offer frame list that summarize information of video data to do so that can remake video from branch that want when need. Need index process of video data for effective video retrieval. This treatise wishes to propose effective method about scene change point detection of video that is been based on contents base index. Proposed method video data so that can grasp whole structure of video detection color value of schedule pixel for diagonal line direction in image sampling do. Data that get into sampling could grasp scene change point on one eye. Color value of pixel that detection in each frame is i frame number by i$\times$j procession to procession A, j stores to reflex height of frame. Introduce MSE and calculate mean error of each frame. If exceed mean error and schedule critical value, wish to detect the frame for scene change point.

• Journal of Animal Science and Technology
• /
• v.61 no.3
• /
• pp.163-169
• /
• 2019

Detection of estrus is an essential factor as a method of successful breeding in the sow. As increasing the adaption of the information and communication technology (ICT) into swine industry, this study focuses on a possibility and quantification of standing time, vulva and body temperature as methods of estrus detection, comparing each time and temperature in estrus and non-estrus period, and analyzing each success rate of new and existing methods. Ultrasonic sensor array and digital infrared thermography were used to evaluate whether new methods such as standing time and number, and vulva and skin temperature can be replaced, or these methods can be quantifiable in estrus period. Ultrasonic sensor array was installed beside the stall and digital infrared thermography was placed in the rear of sow to collect the dates of sow in estrus and non-estrus period. This study showed total standing time, number and number over 10 minutes, and vulva temperature of the sow in estrus period were increased (p < 0.05) compared with those of sow in non-estrus period, respectively. Detection of estrus using standing time and vulva temperature tended (p = 0.06) to increase the success rate when artificial insemination (AI) was performed. In conclusion, standing time and vulva temperature increased when estrus happened. Success rate of AI of sow using these methods showed an increasing trend. Therefore, existing method using the naked eye can be replaced to new method such as vulvar temperature and standing time when detecting the estrus.

• Journal of Advanced Navigation Technology
• /
• v.27 no.6
• /
• pp.865-870
• /
• 2023

Recently, accidents caused by black ice, a road freezing phenomenon caused by natural power, are increasing. Black ice is difficult to identify directly with the human eye and is more likely to misunderstand it as standing water, so there is a high accident rate caused by car sliding. To solve this problem, this paper presents a method of detecting black ice centered on LiDAR sensors. With a small, inexpensive, and high-accuracy light detection and ranging (LiDAR) sensor, the temperature and inclination angle are set differently to detect black ice and asphalt by setting different reflection angles of asphalt and black ice differently in temperatures and inclinations. The LIDARO carried out in the study points out that additional research and improvement are needed to increase accuracy, and through this, more reliable black ice detection methods can be suggested. This method suggests a method of detecting black ice through early system design research by preventing accidents caused by black ice in advance.

• Journal of Forest and Environmental Science
• /
• v.40 no.1
• /
• pp.1-8
• /
• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Journal of KIISE:Software and Applications
• /
• v.27 no.7
• /
• pp.731-741
• /
• 2000

This paper proposes a method to detect a facial region and extract facial features which is crucial for visual recognition of human faces. In this paper, we extract the MER(Minimum Enclosing Rectangle) of a face and facial components using projection analysis on both edge image and binary image. We use an active contour model(snakes) for extraction of the contours of eye, mouth, eyebrow, and face in order to reflect the individual differences of facial shapes and converge quickly. The determination of initial contour is very important for the performance of snakes. Particularly, we detect Minimum Enclosing Rectangle(MER) of facial components and then determine initial contours using general shape of facial components within the boundary of the obtained MER. We obtained experimental results to show that MER extraction of the eye, mouth, and face was performed successfully. But in the case of images with bright eyebrow, MER extraction of eyebrow was performed poorly. We obtained good contour extraction with the individual differences of facial shapes. Particularly, in the eye contour extraction, we combined edges by first order derivative operator and zero crossings by second order derivative operator in designing energy function of snakes, and we achieved good eye contours. For the face contour extraction, we used both edges and grey level intensity of pixels in designing of energy function. Good face contours were extracted as well.

• Journal of the Korean Society of Safety
• /
• v.24 no.6
• /
• pp.72-78
• /
• 2009

Exposure to HDI(hexamethylene di-isocyanate) commonly used in vehicle crash repair workshops remains a leading cause of occupational asthma. Although skin and eye contamination are considered as absorption routes, there are no occupational exposure standards for skin and ocular exposure. This is the reason why there are more empirical data should be provided. Therefore this study was to determine contamination levels of HDI on the skin, eyes, work surfaces, respirators and eye protectors. There was evidence of contamination on a variety of work surfaces, for example, door handles, bench top and spray gun, etc. A high proportion(47~80%) of skin wipe samples from neck, forehead, back hand, palm and wrist was positive for HDI contamination, even though spray time was relatively brief. The contamination levels from spraying inside spray booth were generally higher than outside booth due to poor work practices and inappropriate personal protective use like safety gloves. Apprentices had higher exposure levels than the qualified painters, likely due to lack of the recognition of safety and hygiene. The extent of contamination inside the PPE might provide an indication of the potential for respiratory & skin exposure and ocular exposure. Eye fluid samples from 4 out of 14 workers had the positive detection of HDI contamination, due to poor work practices like no or inappropriate eye protection. Considering the potential for dermal & ocular exposure to contribute to possible health symptoms including respiratory sensitization, the empirical data point to a need for improving work practices and appropriate PPE selection, use and maintenance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.2
• /
• pp.103-108
• /
• 2022

Various studies have been conducted on cloud observation using all-sky images acquired with a wide-angle camera system since the early 21st century, but it is judged that an automatic observation system that can completely replace the eye observation has not been obtained. In this study, to verify the quantification of cloud observation, which is the final step of the algorithm proposed to automate the observation, the cloud distribution of the all-sky image and the corrected image were compared and analyzed. The reason is that clouds are formed at a certain height depending on the type, but like the retina image, the center of the lens is enlarged and the edges are reduced, but the effect of human learning ability and spatial awareness on cloud observation is unknown. As a result of this study, the average cloud observation error of the all-sky image and the corrected image was 1.23%. Therefore, when compared with the eye observation in the decile, the error due to correction is 1.23% of the observed amount, which is very less than the allowable error of the eye observation, and it does not include human error, so it is possible to collect accurately quantified data. Since the change in cloudiness due to the correction is insignificant, it was confirmed that accurate observations can be obtained even by omitting the unnecessary correction step and observing the cloudiness in the pre-correction image.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.55-62
• /
• 2013

Automatic inspection system is required for increment of copper plate production and demand expansion. Thus 3D surface form and defect detection of copper plate calls for 3D image and GUI analysis. Limitation of 2D analysis, such as error occurrence and decision difficulty makes eye inspection automatic. Automatic inspection is able to raise accurate inspection rate and productivity efficiency elevation. In this paper defect classification is defined and inspection system is implemented. Defect analysis algorithms and GUI for 3D image analysis is developed and tested.