• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.117-120
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• 2013

Background: Breast cancer is an increasing health problem in India. Screening for early detection should lead to a reduction in mortality from the disease. It is known that motivation by nurses influences uptake of screening methods by women. This study aimed to investigate knowledge of breast cancer risk factors & early detection methods and the practice of screening among nurses in Indira Gandhi Medical College, Shimla, Himachal Pradesh. Materials and Methods: A cross-sectional study was conducted using a self-administered questionnaire to assess the knowledge of breast cancer risk factors, early detection methods and practice of screening methods among 457 nurses working in a Indira Gandhi Medical College, Shimla-H.P. Chi square test, Data was analysed using SPSS version 16. Test of significance used was chi square test. Results: The response rate of the study was 94.9%. The average knowledge of risk factors about breast cancer of the entire population is 49%. 10.5% of nurses had poor knowledge, 25.2% of the nurses had good knowledge, 45% had very good knowledge and 16.3% of the nurses had excellent knowledge about risk factors of breast cancer and early detection methods. The knowledge level was significantly higher among BSC nurses than nurses with Diploma. 54% of participants in this study reportedly practice BSE at least once every year. Less than one-third reported that they had CBE within the past one year. 7% ever had mammogram before this study. Conclusions: Results from this study suggest the frequent continuing medical education programmes on breast cancer at institutional level is desirable.

• 한국군사과학기술학회지
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• 제12권2호
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• pp.199-205
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• 2009

A simple and effective analysis method is presented for predicting the best infiltration area. Based on geospatial information, numerical estimation barometer for degree of infiltration risk has been derived. The dominant geospatial features influencing infiltration risk have been found to be area altitude, degree of surface gradient, relative direction of surface gradient to the surveillance line, degree of surface gradient repetition, regional forest information. Each feature has been numerically expressed corresponding to the degree of infiltration risk of that area. Four different detection probability maps of infiltration risk for the surveillance area are drawn on the actual map with respect to the numerically expressed five dominant factors of infiltration risks. By combining the four detection probability maps, the complete picture of thr best infiltration area has been drawn. By using the map and the analytic method the effectiveness of surveillance operation can be improved.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• 한국안전학회지
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• 제33권3호
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• pp.83-91
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• 2018

This paper suggests a weibull time delay model to evaluate failure risks in FMEA(failure modes and effects analysis). Assuming three types of loss functions for delayed time in failure cause detection, the risk of each failure cause is evaluated as its occurring frequency and expected loss. Since the closed form solution of the risk metric cannot be obtained, a statistical computer software R program is used for numerical calculation. When the occurrence and detection times have a common shape parameter, though, some simple results of mathematical derivation are also available. As an enormous quantity of field data becomes available under recent progress of data acquisition system, the proposed risk metric will provide a more practical and reasonable tool for evaluating the risks of failure causes in FMEA.

• 한국토양비료학회지
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• 제50권4호
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• pp.235-250
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• 2017

In this study, we monitored 4 volatile organic compounds (VOCs) such as chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane in groundwater samples to determine the detection frequency and their concentrations and evaluated the health risk level considering ingestion, inhalation, and skin contact. 75 groundwater wells were selected. 24 wells were from monitoring background groundwater quality level and 51 wells were from monitoring groundwater quality level in industrial or contamination source area. In the results, the detection frequency for chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane was 42.3%, 8.1%, 6.0%, and 3.4%, respectively. The average concentrations of VOCs were high in the order of chloroform ($1.7{\mu}g\;L^{-1}$), dichloromethane ($0.08{\mu}g\;L^{-1}$), tetrachloromethane ($0.05{\mu}g\;L^{-1}$), and 1,2-dichloroethane ($0.05{\mu}g\;L^{-1}$). Chloroform had the highest detection frequency and average detection concentration. In the contaminated groundwater, the detection frequency of VOCs was high in the order of chloroform, dichloromethane, 1,2-dchloroethane, and tetrachloromethane. The average concentrations for chloroform, dichloromethane, 1,2-dichloroethane, and tetrachloromethane were $2.23{\mu}g\;L^{-1}$, $0.08{\mu}g\;L^{-1}$, $0.07{\mu}g\;L^{-1}$, and $0.06{\mu}g\;L^{-1}$, respectively. All the 4 compounds were detected at industrial complex and storage tank area. The maximum concentration of chloroform, dichloromethane, and 1,2-dichloroethane was detected at industrial complex area. Especially, the maximum concentration of chloroform and dichloromethane was detected at a chemical factory area. In the uncontaminated groundwater, the detection frequency of VOCs was high in the order of chloroform, dichloromethane, and 1,2-dchloroethane and tetrachloromethane was not detected. The average concentrations for chloroform, dichloromethane, and 1,2-dichloroethane were $0.57{\mu}g\;L^{-1}$, $0.07{\mu}g\;L^{-1}$, and $0.03{\mu}g\;L^{-1}$, respectively. Although chloroform in the uncontaminated groundwater was detected the most, the concentration of chloroform was not exceeding water quality standards. By land use, the maximum detection frequency of 1,2-dichloroethane was found near a traffic area. For human risk assessment, the cancer risk for the 4 VOCs was $10^{-6}{\sim}10^{-9}$, while the non-cancer risk (HQ value) for the 4 VOCs is $10^{-2}{\sim}10^{-3}$.

• ETRI Journal
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• 제44권4호
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.228-236
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• 1999

In this paper, we propose visual-based self-localization and obstacle detection algorithms for indoor mobile robots. The algorithms do not require calibration, and can be worked with only single image by using the projective invariant relationship between natural landmarks. We predefine a risk zone without obstacles for a robot, and update the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the averaging image with the current image of a new risk zone. The positions of the robot and the obstacles are determined by relative positioning. The method does not require the prior information for positioning robot. The robustness and feasibility of our algorithms have been demonstrated through experiments in hallway environments.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Asian Pacific Journal of Cancer Prevention
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• 제15권23호
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• pp.10175-10179
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• 2015

Background: The association between the RTEL1 rs6010620 single nucleotide polymorphism (SNP) and glioma risk has been extensively studied. However, the results remain inconclusive. To further examine this association, we performed a meta-analysis. Materials and Methods: A computerized search of the PubMed and Embase databases for publications regarding the RTEL1 rs6010620 polymorphism and glioma cancer risk was performed. Genotype data were analyzed in a meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated to assess the association. Sensitivity analyses, tests of heterogeneity, cumulative meta-analyses, and assessments of bias were performed in our meta-analysis. Results: Our meta-analysis confirmed that risk with allele A is lower than with allele G for glioma. The A allele of rs6010620 in RTEL1 decreased the risk of developing glioma in the 12 case-control studies for all genetic models: the allele model (OR=0.752, 95%CI: 0.715-0.792), the dominant model (OR=0.729, 95%CI: 0.685-0.776), the recessive model (OR=0.647, 95%CI: 0.569-0.734), the homozygote comparison (OR=0.528, 95%CI: 0.456-0.612), and the heterozygote comparison (OR=0.761, 95%CI: 0.713-0.812). Conclusions: In all genetic models, the association between the RTEL1 rs6010620 polymorphism and glioma risk was significant. This meta-analysis suggests that the RTEL1 rs6010620 polymorphism may be a risk factor for glioma. Further functional studies evaluating this polymorphism and glioma risk are warranted.

• 한국ITS학회 논문지
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• 제19권6호
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• pp.61-73
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• 2020

IoT 디바이스로부터 수집된 위치정보를 활용한 실시간 위치센싱 기술은 항만 등 다양한 산업현장에서 활용되고 있다. 그러나 GPS 센서의 특성상 오차는 항상 존재하며, 이를 활용하는 사고위험 검지 알고리즘은 오차의 고려가 필수적이다. 본 연구는 GPS 오차를 고려한 항만 내 낙하물 사고위험 구역 접근검지 알고리즘의 보정 방법론을 제안한다. IoT 디바이스로부터 수집된 GPS 오차 데이터를 확률변수로 하는 확률밀도함수를 추정하였으며 알고리즘의 검증을 위해 미시적 시뮬레이션을 활용하였다. 검증 결과 알고리즘은 디바이스의 위치오차 1m, 5m에 따라 검지 정확도가 각각 93%, 77%로 나타났다. 본 연구는 향후 디바이스의 성능을 고려한 유효 위험범위 설정 및 안전관리에 중요한 역할을 할 수 있을 것으로 기대된다.