• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.297-310
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• 2022

Road facilities such as CCTV poles have potential risk of collision accidents with a car. A collision detection algorithm installed in the facility allows the collision accident to be known remotely. Most collision detection algorithms are operated by simply focusing on whether a collision have occurred, because these methods are used to measure only acceleration data from a 3-axis sensor to detect collision. However, it is difficult to detect other detailed information such as malfunction of the sensor, collision direction and collision strength, because it is not known without witness the accident. Therefore, we proposed enhanced detection algorithm to get the collision direction, and the collision strength from the tilt of the facility after accident using a 9-axis sensor in this paper. In order to confirm the performance of the algorithm, an accuracy evaluation experiment was conducted according to the data measurement cycle and the invocation cycle to an detection algorithm. As a result, the proposed enhanced algorithm confirmed 100% accuracy for 50 weak collisions and 50 strong collisions at the 9-axis data measurement cycle of 10ms and the invocation cycle of 1,000ms. In conclusion, the algorithm proposed is expected to provide more reliable and detailed information than existing algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.54-57
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• 2022

Korea has a vulnerable environment from the risk of wildfires, which causes great damage every year. To prevent this, a lot of manpower is being used, but the effect is insufficient. If wildfires are detected and extinguished early through artificial intelligence technology, damage to property and people can be prevented. In this paper, we studied the object double detection method with the goal of minimizing the data collection and processing process that occurs in the process of creating an object detection model to minimize the damage of wildfires. In YOLOv5, the original image is primarily detected through a single model trained on a limited image, and the object detected in the original image is cropped through Crop. The possibility of improving the false positive object detection rate was confirmed through the object double detection method that re-detects the cropped image.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.829-838
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• 2015

As the number of smartphone users have increased, many kinds of malwares have emerged. Unlike existing malwares, spyware can be installed normally after user authentication and agreement according to security policy. For this reason, it is not easy to catch spywares involving harmful functionalities to users by using existing malware detection system. Therefore, our paper focuses on study about detecting mainly wiretapping spywares among them by developing a new wiretapping detection model and application. Specifically, this study conducts to find out power consumption on each application and modular and network consumption to detect voice wiretapping so Open Source Project Power Tutor is used to do this. The risk assessment of wiretapping is measured by gathered all power consumption data from Open Source Project Power Tutor. In addition, developed application in our study can detect at-risk wiretapping spyware through collecting and analyzing data. After we install the application to the smartphone, we collect needed data and measure it.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.179-187
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• 2003

This study investigate the reasons of organizational failure in detection and appropriate response to risk signal. The Crisis does not come true suddenly, there is some risk signals in crisis. If Organization detect the risk signals the crisis is come true opportunities, if not the crisis is come true disastrous outcome. This is use the system dynamics approach. System Dynamics assume the system as a collection of causal feedback loop, so we understand the dynamics around the problems. This investigate suggest that, the focus on growth is the a kind of promotional pressure and the pressure drive the organization to less attention the risk signal, so the risk is underestimate In proportion to real risk. Ultimate, the organization entrap the promotional climate and insensible to security. This study is a kind of hypothesis-discovering research, in the further study, the discovered hypothesis will be empirically tested.

• Journal of Environmental Health Sciences
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• v.39 no.1
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• pp.32-42
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• 2013

Objectives: Cryptosporidium, a protozoan parasite, has been recognized as a frequent cause of waterborne disease due to its extremely strong resistance against chlorine disinfection. Although there has as yet been no report of a Cryptosporidium outbreak through drinking water in Korea, it is important to estimate the health risk of Cryptosporidium in water supply systems because of the various infection cases in human and domestic animals and frequent detection reports on their oocysts in water environments. Methods: This study evaluated the annual infection risk of Cryptosporidium in tap water using the quantitative microbial risk assessment technique. Exposure assessment was performed upon the results of a national survey on Cryptosporidium on the water sources of 97 large-scale water purification plants in Korea, water treatment efficacy, and daily unboiled tap water consumption. The estimates of the US Environmental Protection Agency on the mean likelihood of infection from ingesting one oocyst were applied for effect assessment. Results: Using probabilistic methods, mean annual infection risk of Cryptosporidiosis by the intake of tap water was estimated to fall within the range of $2.3{\times}10^{-4}$ to $1.0{\times}10^{-3}$ (median $5.7{\times}10^{-4}$). The risk in using river sources was predicted to be four times higher than with lake sources. With 0.5-log higher removal efficacy, the risk was estimated to be $1.8{\times}10^{-4}$, and could then be lowered by one-third. Conclusions: These estimations can be compared with acceptable risk and then used to determine the adequacy and priority of various drinking water quality strategies such as the establishment of new treatment technology.

• Journal of Chest Surgery
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• v.57 no.3
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• pp.302-311
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• 2024

Background: Unexpected conversion to thoracotomy during planned video-assisted thoracoscopic surgery (VATS) can lead to poor outcomes and comparatively high morbidity. This study was conducted to assess preoperative risk factors associated with unexpected thoracotomy conversion and to develop a risk scoring model for preoperative use, aimed at identifying patients with an elevated risk of conversion. Methods: A retrospective analysis was conducted of 1,506 patients who underwent surgical resection for non-small cell lung cancer. To evaluate the risk factors, univariate analysis and logistic regression were performed. A risk scoring model was established to predict unexpected thoracotomy conversion during VATS of the lung, based on preoperative factors. To validate the model, an additional cohort of 878 patients was analyzed. Results: Among the potentially significant clinical variables, male sex, previous ipsilateral lung surgery, preoperative detection of calcified lymph nodes, and clinical T stage were identified as independent risk factors for unplanned conversion to thoracotomy. A 6-point risk scoring model was developed to predict conversion based on the assessed risk, with patients categorized into 4 groups. The results indicated an area under the receiver operating characteristic curve of 0.747, with a sensitivity of 80.5%, specificity of 56.4%, positive predictive value of 1.8%, and negative predictive value of 91.0%. When applied to the validation cohort, the model exhibited good predictive accuracy. Conclusion: We successfully developed and validated a risk scoring model for preoperative use that can predict the likelihood of unplanned conversion to thoracotomy during VATS of the lung.

• Journal of Korean Academy of Nursing
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• v.33 no.8
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• pp.1119-1126
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• 2003

Background. Breast cancer is the most common form of cancer among Korean women. Only 14 % of urban women and 10% of rural women in Korea, however, participated in breast cancer screening behavior in 1998 (Korean Ministry of Health & Welfare, 1999). Purpose. The aim of this study was to evaluate the effect of community-based breast self-examination (BSE) education programs in Korea. Methods. First, breast cancer risk appraisals were done with 1,977 rural women. Of the 1,977 women, nearly 30% (n=494) had a higher or equal to borderline risk of developing breast cancer. This quasi-experimental study was conducted to target these women with a high or equal to borderline risk of breast cancer. The risk appraisal feedback and breast self-examination education were used as an intervention for breast cancer prevention and early detection. Results. After a 3-month follow-up, 30.5% of the women in the intervention group performed regular BSE compared to 10.2 % of women in the control group. The mean knowledge score related to breast cancer and BSE was significantly higher for the women in the intervention group than that in the control group.

• Environmental Analysis Health and Toxicology
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• v.26
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• pp.2.1-2.7
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• 2011

Objectives: N-Nitrosodimethylamine (NDMA) is classified as a probable human carcinogen by the United States Environmental Protection Agency (US EPA) and is formed during the chlorination of municipal drinking water. In this study, selected nitrosamines were measured in chlorinated drinking water collected from Chuncheon, Kangwon-do, Republic of Korea, and a risk assessment for NDMA was conducted. Methods: Twelve water samples were collected from 2 treatment plants and 10 household taps. Samples were analyzed for 6 nitrosamines via solid-phase extraction cleanup followed by conversion to dansyl derivatives and high-performance liquid chromatography-fluorescence detection (HPLC-FLD). Considering the dietary patterns of Korean people and the concentration change of NDMA by boiling, a carcinogenic risk assessment from ingestion exposure was conducted following the US EPA guidelines. Results: NDMA concentrations ranged between 26.1 and 112.0 ng/L. NDMA in water was found to be thermally stable, and thus its concentration at the end of boiling was greater than before thermal treatment owing to the decrease in water volume. The estimated excess lifetime carcinogenic risk exceeded the regulatory baseline risk of $10^{-5}$. Conclusions: This result suggests that more extensive studies need to be conducted on nitrosamine concentration distributions over the country and the source of relatively high nitrosamine concentrations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.101-111
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• 2004

Risk analysis process, which identifies vulnerabilities and threat causes of network assets and evaluates expected loss when some of network assets are damaged, is essential for diagnosing ISP network security levels and response planning. However, most existing risk analysis systems provide only methodological analysis procedures, and they can not reflect continually changing vulnerabilities and threats information of individual network system on real time. For this reason, this paper suggests new system design methodology which shows a scheme to collects and analyzes data from network intrusion detection system and vulnerability analysis system and estimate quantitative risk levels. Additionally, experimental performance of proposed system is shown.