• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.127-133
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• 2002

Most structures are constantly influenced by repeated weathering phenomena and load action during a period of its utilization, and their component materials become superannuated and their design performance becomes slowly lost. Therefore, it is thought that it is necessary to develop the online monitoring system that can make a great contribution to the maintenance management and disaster prevention of the building structure by sensing any slight change of the entire structure regardless of its inside and outside. Especially, this study was intended to explain the entire system of the online structure by interpreting welded built-up H-beams, of structural steel members having many advantages in terms of the qualities of structural materials, through limit state design, and presenting the basic plan to apply it to the structure.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Geomechanics and Engineering
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• v.11 no.1
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• pp.41-58
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• 2016

This paper focuses on the deformation behavior of tunnels crossing a weak zone in conventional tunneling. A three-dimensional finite element model was adopted that allows realistic modeling of the tunnel excavation and the support installation. Using the 3D FE model, a parametric study was conducted on a number of tunneling cases with emphasis on the spatial characteristics of the weak zone such as the strike and dip angle, and on the initial stress state. The results of the analyses were thoroughly examined so that the three-dimensional tunnel displacements at the tunnel crown and the sidewalls can be related to the spatial characteristic of the weak zone as well as the initial stress state. The results indicate that the effectiveness of the absolute displacement monitoring data as early warning indicators depends strongly on the spatial characteristics of the weak zone. It is also shown that proper interpretation of the absolute monitoring data can provide not only early warning for a weak zone outside the excavation area but also information on the orientation and the extent of the weak zone. Practical implications of the findings are discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.888-891
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• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1661-1668
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• 2020

A physical protection system (PPS) is used for the protection of critical facilities. This paper proposes a structure analytic hierarchy approach (SAHA) for the hierarchical evaluation of the PPS effectiveness in critical infrastructure. SAHA is based on the traditional analysis methods "estimate of adversary sequence interruption, EASI". A community algorithm is used in the building of the SAHA model. SAHA is applied to cluster the associated protection elements for the topological design of complicated PPS with graphical vertexes equivalent to protection elements.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.505-513
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• 2006

One of the major reasons for recent blackout, like August 14, 2003 blackout in the US and Canada has been insufficient voltage/reactive power support. For the stable reactive power management, a new approach for the voltage monitoring and control structure is required in the market environment. This paper proposes the effective localized-voltage control scheme using the information from pilot buses at each zone. In this paper, the steady state voltage monitoring and control (SSVMC) is adopted and illustrated for the voltage control scheme during steady state because it is thought as the systemic algorithm to explain voltage profile phenomenon before and after contingencies. And the concept of electrical distance is applied to simultaneously achieve both clustering the voltage control zone, and selecting the pilot bus as the representative node at each control zone. Applying SSVMC based on the structure with clustering and pilot bus enables system operators to monitor and understand the system condition much more easily, to monitor and control the voltage in real-time more manageably, and to respond quickly to a disturbance. The proposed voltage control scheme has been tested on the IEEE 14-bus system with the numerical analysis to examine the system reliability and structure efficiency.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2638-2651
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• 2020

Nozzle corner cracks present at the intersection of reactor pressure vessels (RPVs) and inlet or outlet nozzles have been a persistent problem for a number of years. The fracture analysis of such nozzle corner cracks is very important and critical for the efficient design and assessment of the structural integrity of RPVs. This paper aims to perform an engineering critical assessment of RPVs with nozzle corner cracks subjected to several transients accompanied by pressurized thermal shocks. The critical crack size of the RPV model with nozzle corner cracks under transient loading is evaluated on failure assessment curve. In particular, the influence of cladding on the crack initiation of nozzle corner crack under thermal transients is studied. The influence of primary internal pressure and secondary thermal stress on the stress field at nozzle corner and SIF at crack front is analyzed. Finally, the influence of different crack size and crack shape on the final critical crack size is analyzed.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1454-1463
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• 2021

Demographics, personality traits and attitudes are related to safety behaviors in varied workplaces, but their roles in nuclear power plants (NPPs) have not been fully understood. This study was conducted to explore the roles of a set of demographic, personality and attitudinal factors on self-reported safety behaviors (including safety participation and human errors) among NPP commissioning workers. Survey data were collected from 157 Chinese commissioning workers. Results showed that age and work experience were significantly associated with human errors, but not with safety participation. Neuroticism and conscientiousness were significantly related to human errors, while neuroticism, conscientiousness and agreeableness were significantly related to safety participation. Attitude towards questioning was observed as an antecedent of safety participation, and functioned as a mediating variable in the relation between conscientiousness and safety behaviors. The findings provide evidence-based implications on the design of diverse interventions and strategies for the promotion of safety behaviors in NPPs.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.281-286
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• 2015

The importance of air quality monitoring is rapidly increasing. Even though state-of-the-art air quality monitoring technologies such as mass spectrometry, gas chromatography, and optical measurement enable high-fidelity measurement of air pollutants, they cannot be widely used for portable or personalized platforms because of their high cost and complexity. Recently, personalized and localized environmental monitoring, rather than global and averaged environmental monitoring, has drawn greater attention with the advancement of mobile telecommunication technologies. Here, micro- and nano-technologies enable highly integrated and ultra-compact sensors to meet the needs of personalized environmental monitoring. In this paper, several examples of MEMS-based gas sensors for compact and personalized air quality monitoring are explained. Additionally, the principles and usage of functional nanomaterials are discussed for highly sensitive and selective gas sensors.