• Safety and Health at Work
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• v.12 no.3
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• pp.296-303
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• 2021

Background: Rapid population aging in developed countries has resulted in the working-age population increasingly being tasked with the provision of informal care. Methods: An educational intervention was delivered to 21 carer-employees employed at a Canadian University. Work role function, job security, schedule control, work-family conflict, familywork conflict, and supervisor and coworker support were measured as part of an aggregated workplace experience score. This score was used to measure changes pre/post intervention and at a follow-up period approximately 12 months post intervention. Three random intercept models were created via linear mixed modeling to illustrate changes in participants' workplace experience across time. Results: All three models reported statistically significant random and fixed effects intercepts, with a positive coefficient of change. Conclusion: This suggests that the intervention demonstrated an improvement of the workplace experience score for participants over time, with the association particularly strong immediately after intervention.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.229-239
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• 2008

Most algorithms for detecting incidents have been developed under the premise that congestion must happen whenever an incident occurs. For that reason, the performance of these algorithms could not be guaranteed in cases where congestion did not happen due to traffic operations with low flows despite the occurrence of an incident. The objective of this paper is to develop an automatic incident detection algorithm using a new diagram that can reliably detect the incident under various conditions of traffic operations including a low volume state. Compared with the McMaster Algorithm, the proposed algorithm in this paper was evaluated with three different cases in which the incidents occur in traffic operations with a low volume state, a relatively high volume state, and a recurrent congestion state. It is shown that the new algorithm has a capability to identify the flow characteristics of incidents for all the three cases and is much better than McMaster algorithm in terms of detection rate and false alarm rate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1540-1553
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• 2019

A nuclear power plant (NPP) is a highly complex system-of-systems as manifested through its internal systems interdependence. The negative impact of such interdependence was demonstrated through the 2011 Fukushima Daiichi nuclear disaster. As such, there is a critical need for new strategies to overcome the limitations of current risk assessment techniques (e.g. the use of static event and fault tree schemes), particularly through simulation of the nonlinear dynamic feedback mechanisms between the different NPP systems/components. As the first and key step towards developing an integrated NPP dynamic probabilistic risk assessment platform that can account for such feedback mechanisms, the current study adopts a system dynamics simulation approach to model the thermal dynamic processes in: the reactor core; the secondary coolant system; and the pressurized water reactor. The reactor core and secondary coolant system parameters used to develop system dynamics models are based on those of the Palo Verde Nuclear Generating Station. These three system dynamics models are subsequently validated, using results from published work, under different system perturbations including the change in reactivity, the steam valve coefficient, the primary coolant flow, and others. Moving forward, the developed system dynamics models can be integrated with other interacting processes within a NPP to form the basis of a dynamic system-level (systemic) risk assessment tool.

• Journal of Acupuncture Research
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• v.29 no.6
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• pp.111-117
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• 2012

Objectives : This study was done for reporting effect of moxibustion therapy on the degenerative arthritis of knee joint with osteochondroma. Methods : Two patient with degenerative arthritis of knee joint with osteochondroma was treated by moxibustion therapy. We applied moxibustion therapy three times a day for 12 days(three days per week). To investigate effectiveness of treatment we used visual analogue scale, Korean Western Ontario and McMaster Universities. Results : 1. The moxibustion therapy deceased knee joint pain. 2. As a result of evaluation by visual analogue scale, Korean Western Ontario and McMaster Universities the score marked lower than before treatment and after treatment. Conclusion : Moxibustion therapy decreased knee joint pain that patient have degenerative arthritis of knee joint with osteochondroma.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.121-124
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• 2015

본 논문은 Multiscale Gradients (MSG)를 기반으로 한 Color Filter Array Interpolation을 배경으로 Kodak Dataset보다 실제 디지털 카메라로 촬영한 이미지에 가까운 McMaster Dataset에서 개선된 성능을 내는 알고리듬을 제안한다. MSG는 녹색 채널 보간, 녹색 채널 갱신, 빨간색, 파란색 채널 보간의 과정을 거친다. 이때 높은 스펙트럼 상관관계, 낮은 색채도, 낮은 색 경사도를 가진 Kodak Dataset과 달리 자연 이미지에서는 녹색 채널 갱신 과정의 추정방법을 사용하면 화질 및 Color Peak Signal to Noise Ratio (CPSNR)이 저하되는 것을 확인하였다. 이러한 실험결과를 바탕으로 개선된 필터와 색상 보간 과정을 통해 기존의 알고리듬에 비해 향상된 성능을 보여주는 알고리듬을 제안한다.

• Structural Engineering and Mechanics
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• v.4 no.1
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• pp.73-89
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• 1996

In Lagrangian mechanics, attention is directed at the body as it moves through space. The region occupied by the body is called a configuration. All body points are labelled by the position they would have if the body were to occupy a chosen reference configuration. The reference configuration can be regarded as an extra fictional copy where notes are kept. As the body moves and deforms, it is important to correctly observe the use of each configuration for computational purposes. The description of strain is particularly important. The present work establishes clearly the role of each configuration in total and in incremental forms. This work also details the differences between gradient and configurational calculus.

• Structural Engineering and Mechanics
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• v.4 no.1
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• pp.91-107
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• 1996

In Lagrangian mechanics, attention is directed at the body as it moves through space. Each body point is identified by the position it would have if the body were to occupy an arbitrary reference configuration. A result of this approach is that the analyst often describes the body by using quantities that may involve more than one configuration. This is particularly common in incremental calculations and in changes of the choice of reference configuration. With the rise of very powerful computing machinery, the popularity of numerical calculation has become great. Unfortunately, the mechanical theory has been evolved in a piecemeal fashion so that it has become a conglomeration of differently developed patches. The current work presents a unified development of the equilibrium principle. The starting point is the conservation of momentum. All details of configuration are shown. Finally, full dynamic and static forms are presented for total and incremental work.

• Physical Therapy Korea
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• v.14 no.1
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• pp.82-89
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• 2007

The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) is a valid and widely used instrument for the assessment of osteoarthritis patients. In this study, data was obtained from the out-patients with painful osteoarthritis of the knee. One hundred-three out-patients were interviewed by physical therapists. In an exploratory way, a Korean version of the KWOMAC was analyzed for unidimensionality, item separation, and item difficulty using the Winsteps programs. Ninety-five patients with osteoarthritis of the knee over 65 years were analyzed for Rash analysis. In the analysis several functional items poorly fit to the model. These items included "heavy domestic duties" and "standing". In the pain domain, one item ("at night while in bed") did not fit the model. In the stiffness domain one item ("after sitting, lying, or resting later in the day") did not fit the model. Although 4 items from the 3 domains (pain, stiffness, function domain) do not fit well, the KWOMAC domains were confirmed by Rasch analysis. Thus the KWOMAC needs to be further examined before it can be used to properly determine the health status of the elderly with OA.