• Journal of Computing Science and Engineering
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• 제10권3호
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• pp.75-84
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• 2016

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.

• 한국생산제조학회지
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• 제26권3호
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• pp.272-277
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• 2017

CCTV image processing techniques have been developed for safety management in manufacturing sites. However, CCTV growth has become a social problem for video surveillance with regard to privacy. This study aims to manage the safety system efficiently and protect privacy simultaneously. In this study, the CCTV monitoring system is composed of five steps (accident monitoring, detection, notification, management, restoration). De-identified image is observed when we are in a normal situation. De-identified image changes to identified image when it detects an accident. As soon as it detects an accident, the accident information is sent to the safety administrator. Then the administrator could conduct safety measures. Afterward, accumulated accident data could be used for statistical data that could be utilized as analyzing expecting accident.

• 한국산학기술학회논문지
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• 제12권4호
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• pp.1940-1946
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• 2011

본 논문에서는 최근 증가하고 있는 학교주변의 사고 및 범죄사건에 대한 예방 및 관리를 위한 스쿨존 안전관리 방안에 대해 제안한다. 스쿨존 안전관리를 위하여 센서 네트워크 기반의 지능형 감시모듈에 대한 설계가 고려되어야 한다. 이는 단순히 CCTV 감시 방식 기반이 아닌 위험 관리를 위한 상황을 감지하고 분석하는 기능으로의 확대를 의미한다. 따라서 학교 주변상황에서 발생하는 여러 가지 상황을 고려한 감시모듈 설계가 이루어져야 하며, 본 논문에서는 이에 대한 방안을 제시한다.

• Nuclear Engineering and Technology
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• 제52권2호
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• pp.381-396
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• 2020

Investigations of the commercial aircraft impact effect on nuclear island infrastructures have been drawing extensive attention, and this paper aims to perform the safety assessment of Generation III nuclear power plant (NPP) buildings subjected to typical commercial aircrafts crash. At present Part I, finite element (FE) models establishment and validations for both the aircrafts and NPP buildings are performed. (i) Airbus A320 and A380 aircrafts are selected as the representative medium and large commercial aircrafts, and the corresponding fine FE models including the skin, beam, fuel and etc. are established. By comparing the numerically derived impact force time-histories with the existing published literatures, the rationality of aircrafts models is verified. (ii) Fine FE model of the Chinese Zhejiang Sanao NPP buildings is established, including the detailed structures and reinforcing arrangement of both the containment and auxiliary buildings. (iii) By numerically reproducing the existing 1/7.5 scaled aircraft model impact tests on steel plate reinforced concrete (SC) panels and assessing the impact process and velocity time-history of aircraft model, as well as the damage and the maximum deflection of SC panels, the applicability of the existing three concrete constitutive models (i.e., K&C, Winfrith and CSC) are evaluated and the superiority of Winfrith model for SC panels under deformable missile impact is verified. The present work can provide beneficial reference for the integral aircraft crash analyses and structural damage assessment in the following two parts of this paper.

• Nuclear Engineering and Technology
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• 제52권2호
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• pp.397-416
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• 2020

Investigations of the commercial aircraft impact effect on nuclear island infrastructures have been drawing extensive attention, and this paper aims to perform the safety assessment of Generation III nuclear power plant (NPP) buildings subjected to typical commercial aircrafts crash. At present Part II, based on the verified finite element (FE) models of aircrafts Airbus A320 and A380, as well as the NPP containment and auxiliary buildings in Part I of this paper, the whole collision process is reproduced numerically by adopting the coupled missile-target interaction approach with the finite element code LS-DYNA. The impact induced damage of NPP plant under four impact locations of containment (cylinder, air intake, conical roof and PCS water tank) and two impact locations of auxiliary buildings (exterior wall and roof of spent fuel pool room) are evaluated. Furthermore, by considering the inner structures in the containment and raft foundation of NPP, the structural vibration analyses are conducted under two impact locations (middle height of cylinder, main control room in the auxiliary buildings). It indicates that, within the discussed scenarios, NPP structures can withstand the impact of both two aircrafts, while the functionality of internal equipment on higher floors will be affected to some extent under impact induced vibrations, and A380 aircraft will cause more serious structural damage and vibrations than A320 aircraft. The present work can provide helpful references to assess the safety of the structures and inner equipment of NPP plant under commercial aircraft impact.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• 한국전자거래학회지
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• 제14권3호
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• pp.59-70
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• 2009

유비쿼터스 컨버전스 산업의 가능성을 확인하기 위한 시험대로서 u-City에 대한 관심이 지속적으로 커져가고 있는 시점에서, u-City의 대표적인 서비스 및 응용 분야로서 u-방재에 대한 연구도 서서히 확대되어가고 있다. u-City의 다른 영역과 마찬가지로 u-방재 서비스의 원활한 제공을 위해서는 기존에 분산되어 운영되고 있는 다양한 u-City 서비스 및 하부 인프라가 종합적으로 연계될 수 있는 방법이 매우 중요하다. 본 연구에서는 분산자원관리 분야에서 널리 활용되고 있는 CIM/WBEM 표준과 대용량 모니터링 데이터의 취득 및 활용에 적절한 모델로 인정되고 있는 GMA를 결합하여 u-방재 서비스 및 모니터링 아키텍처의 설계에 적용하는 방안을 제시하고자 한다. 향후 u-City 서비스 인프라의 표준화 및 확장성에 대한 요구가 더욱 커져갈 것으로 예상된다는 점에서, 본 연구에서 제시하는 통합 인프라 모델은 기존에 구축되어 있는 분산자원들과 지속적으로 추가될 것으로 예상되는 신규 서비스들의 효과적인 통합 모델로써 활용될 수 있을 것이다.

• Nuclear Engineering and Technology
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• 제36권6호
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• pp.528-539
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• 2004

It is essential in commercial reactors that the safety limits imposed on the fuel pellets and fuel clad barriers, such as the linear power density (LPD) and the departure from nucleate boiling ratio (DNBR), are not violated during reactor operations. In order to accurately monitor the safety limits of current reactor states, a detailed three-dimensional (3D) core power distribution should be estimated from the in-core detector signals. In this paper, we propose a calculation methodology for detailed 3D core power distribution, using in-core detector signals and core monitoring constants such as the 3D Coupling Coefficients (3DCC), node power fraction, and pin-to-node factors. Also, the calculation method for several core safety parameters is introduced. The core monitoring constants for the real core state are promptly provided by the core design code and on-line MASTER (Multi-purpose Analyzer for Static and Transient Effects of Reactors), coupled with the core monitoring program. through the plant computer, core state variables, which include reactor thermal power, control rod bank position, boron concentration, inlet moderator temperature, and flow rate, are supplied as input data for MASTER. MASTER performs the core calculation based on the neutron balance equation and generates several core monitoring constants corresponding to the real core state in addition to the expected core power distribution. The accuracy of the developed method is verified through a comparison with the current CECOR method. Because in all the verification calculation cases the proposed method shows a more conservative value than the best estimated value and a less conservative one than the current CECOR and COLSS methods, it is also confirmed that this method secures a greater operating margin through the simulation of the YGN-3 Cycle-1 core from the viewpoint of the power peaking factor for the LPD and the pseudo hot pin axial power distribution for the DNBR calculation.

• Safety and Health at Work
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• 제10권1호
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• pp.95-102
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• 2019

Background: In a cluster-randomized trial, the Safety and Health Involvement For Truck drivers intervention produced statistically significant and medically meaningful weight loss at 6 months (-3.31 kg between-group difference). The current manuscript evaluates the relative impact of intervention components on study outcomes among participants in the intervention condition who reported for a post-intervention health assessment (n = 134) to encourage the adoption of effective tactics and inform future replications, tailoring, and enhancements. Methods: The Safety and Health Involvement For Truck drivers intervention was implemented in a Web-based computer and smartphone-accessible format and included a group weight loss competition and body weight and behavioral self-monitoring with feedback, computer-based training, and motivational interviewing. Indices were calculated to reflect engagement patterns for these components, and generalized linear models quantified predictive relationships between participation in intervention components and outcomes. Results: Participants who completed the full program-defined dose of the intervention had significantly greater weight loss than those who did not. Behavioral self-monitoring, computer-based training, and health coaching were significant predictors of dietary changes, whereas behavioral and body weight self-monitoring was the only significant predictor of changes in physical activity. Behavioral and body weight self-monitoring was the strongest predictor of weight loss. Conclusion: Web-based self-monitoring of body weight and health behaviors was a particularly impactful tactic in our mobile health intervention. Findings advance the science of behavior change in mobile health intervention delivery and inform the development of health programs for dispersed populations.