• 한국멀티미디어학회논문지
• /
• 제23권2호
• /
• pp.351-360
• /
• 2020

If various unreasonable safety standards are preemptively and effectively readjusted, the risk of accidents can be reduced. In this paper, we proposed a machine reading comprehension-based safety standard Q&A system to secure supporting technology for effective search and analysis of safety standards for integrated and systematic management of safety standards. The proposed model finds documents related to safety standard questions in the various laws and regulations, and then divides these documents into provisions. Only those provisions that are likely to contain the answer to the question are selected, and then the BERT-based machine reading comprehension model is used to find answers to questions related to safety standards. When the proposed safety standard Q&A system is applied to KorQuAD dataset, the performance of EM 40.42% and F1 55.34% are shown.

• Journal of Electrical Engineering and Technology
• /
• 제11권4호
• /
• pp.869-877
• /
• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• 한국해양공학회지
• /
• 제35권3호
• /
• pp.203-215
• /
• 2021

Current industrial practices and approaches are simplified and do not describe the actual behavior of plated elements of offshore topside structures for safety design due to fires. Therefore, it is better to make up for the defective methods with integrated fire safety design methods based on fire resistance characteristics such as residual strength capacity. This study numerically investigates the residual strength of steel stiffened panels exposed to hydrocarbon jet fire. A series of nonlinear finite element analyses (FEAs) were carried out with varying probabilistic selected exposures in terms of the jet fire location, side, area, and duration. These were used to assess the effects of exposed fire on the residual strength of a steel stiffened panel on a ship-shaped offshore structure. A probabilistic approach with a feasible fire location was used to determine credible fire scenarios in association with thermal structural responses. Heat transfer analysis was performed to obtain the steel temperature, and then the residual strength was obtained for the credible fire scenarios under compressive axial loading using nonlinear FEA code. The results were used to derive closed-form expressions to predict the residual strength of steel stiffened panels with various exposure to jet fire characteristics. The results could be used to assess the sustainability of structures at risk of exposure to fire accidents in offshore installations.

• 임상간호연구
• /
• 제27권1호
• /
• pp.109-119
• /
• 2021

Purpose: The purpose of this study was to identify and analyze the characteristics of nurses' medication errors during three years. Methods: Retrospective survey study design was used to analyze medication errors by nurses among patient safety accidents. Data were collected for three years from January, 2017 to December, 2019. Data were analyzed using frequency, percentage, 𝑥²-test, and logistic regression with SPSS 26.0 program. Results: Of a total 677 medication errors, 40.6% were caused by nurses. Among the medication errors, near miss (n=154, 56.0%), intravenous bolus injection (n=170, 61.8%), wrong dose (n=102, 37.1%) and carelessness for repetitive work (n=98, 35.6%) were the most common. Medication errors differed by department, and nurses' career, and patient safety accident type. The results of the logistic regression analysis showed that the risk factors of adverse events were medication of fluids (OR=3.93, 95% CI: 1.26~12.27), insulin subcutaneous injection (OR=39.06, 95% CI: 4.58~333.18), and occurrence of extravasation/infiltration (OR=7.26, 95% CI: 1.85~28.53). Conclusion: The simplest and most effective way to prevent medication errors is to keep 5 right, and a differentiated education program according to department and nurse career is needed rather than general education programs. Hospital-level integrated interventions such as a medication barcode system or a team nursing method are also necessary.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.171-176
• /
• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• 대한한방내과학회지
• /
• 제43권6호
• /
• pp.1149-1161
• /
• 2022

Purpose: This systematic review was conducted to evaluate the clinical effects of Injinho-tang on hyperbilirubinemia in hepatobiliary disorders. Methods: We searched for randomized controlled clinical trials that had administered Injinho-tang as an intervention in the following medical databases: Public/Publisher MEDLINE (PubMed), Excerpta Medica dataBASE (EMBASE), Cochrane library, Research Information Sharing Service (RISS), ScienceON, Oriental Medicine Advanced Searching Integrated System (OASIS), and China National Knowledge Infrastructure (CNKI). Among the retrieved studies, only trials that met the inclusion criteria were selected, and serum total bilirubin values were extracted and analyzed from the finally selected trials. Results: The serum total bilirubin values of 1,302 patients with various hepatobiliary diseases were synthesized through a meta-analysis, which confirmed a decrease in serum total bilirubin of 21.03 𝜇mol/L (95% CI -29.58~-12.49, p<0.01) in the group administered with Injinho-tang compared with the control group. Conclusions: Injinho-tang is effective in alleviating hyperbilirubinemia in hepatobiliary diseases when administered with conventional treatment. However, the potential risk of bias, high heterogeneity among the included trials, and differences in herbal composition are limitations of the results of this meta-analysis.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.721-727
• /
• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

• Nuclear Engineering and Technology
• /
• 제55권3호
• /
• pp.801-813
• /
• 2023

The events at Three Mile Island in the United States brought about fundamental changes in the ways that simulation would be used in nuclear operations. The need for research simulators was identified to scientifically study human-centered risk and make recommendations for process control system designs. This paper documents the human factors research conducted at the Human Systems and Simulation Laboratory (HSSL) since its inception in 2010 at Idaho National Laboratory. The facility's primary purposes are to provide support to utilities for system upgrades and to validate modernized control room concepts. In the last decade, however, as nuclear industry needs have evolved, so too have the purposes of the HSSL. Thus, beyond control room modernization, human factors researchers have evaluated the security of nuclear infrastructure from cyber adversaries and evaluated human-in-the-loop simulations for joint operations with an integrated hydrogen generation plant. Lastly, our review presents research using human reliability analysis techniques with data collected from HSSL-based studies and concludes with potential future directions for the HSSL, including severe accident management and advanced control room technologies.

• Tuberculosis and Respiratory Diseases
• /
• 제87권1호
• /
• pp.91-99
• /
• 2024

Background: Tuberculous pleural effusion (TPE) and parapneumonic effusion (PPE) are often difficult to differentiate owing to the overlapping clinical features. Observational studies demonstrate that the ratio of lactate dehydrogenase to adenosine deaminase (LDH/ADA) is lower in TPE compared to PPE, but integrated analysis is warranted. Methods: We conducted a systematic review to evaluate the diagnostic accuracy of the LDH/ADA ratio in differentiating TPE and PPE. We explored the PubMed and Scopus databases for studies evaluating the LDH/ADA ratio in differentiating TPE and PPE. Results: From a yield of 110 studies, five were included for systematic review. The cutoff value for the LDH/ADA ratio in TPE ranged from <14.2 to <25. The studies demonstrated high heterogeneity, precluding meta-analysis. Quality Assessment of Diagnostic Accuracy Studies Tool 2 assessment revealed a high risk of bias in terms of patient selection and index test. Conclusion: LDH/ADA ratio is a potentially useful parameter to differentiate between TPE and PPE. Based on the limited data, we recommend an LDH/ADA ratio cutoff value of <15 in differentiating TPE and PPE. However, more rigorous studies are needed to further validate this recommendation.

• Nuclear Engineering and Technology
• /
• 제56권1호
• /
• pp.222-232
• /
• 2024

New modular factory-built methodologies implemented in the construction and industrial plant industries may bring down costs for modular reactors. A factory-built environment brings about benefits such as; improved equipment, tools, quality, shift patterns, training, continuous improvement learning, environmental control, standardisation, parallel working, the use of commercial off shelf equipment and much of the commissioning can be completed before leaving the factory. All these benefits combine to reduce build schedules, increase certainty, reduce risk and make financing easier and cheaper.Currently, the construction and industrial chemical plant industries have implemented successful modular design and construction techniques. Therefore, the objectives of this paper are to understand and analyse the state of the art research in these industries through a systematic literature review. The research can then be assessed and applied to modular reactors.The literature review highlighted analysis methods that may prove to be useful. These include; modularisation decision tools, stakeholder analysis, schedule, supply chain, logistics, module design tools and construction site planning. Applicable research was highlighted for further work exploration for designers to assess, develop and efficiently design their modular reactors.