• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.63.1-63.1
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• 2011

To analyze the phenomenon of corrosion in the PV module, we experimented damp heat test at $85^{\circ}C$/85% relative humidity(RH) and $65^{\circ}C$/85% RH for 2,000 hours, respectively. We used 30 mini-modules designed of 6inch one cell. Despite of 2,000 hours test, measured $P_{max}$ is not reached failure which is defined less than 80% compared to initial $P_{max}$. Therefore, we calculate proper curve fitting over 2,000 hours. Data less than 80% $P_{max}$ is found and B10 lifetime is calculated by the number of failure specimens and weibull distribution. Using B10 lifetime that the point of failure rate 10% and Peck's model, the predictable equation of lifetime was derived under temperature and humidity condition.

• Journal of Korean Society of Steel Construction
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• v.14 no.2
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• pp.365-373
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• 2002

In this paper, the system reliability concept was presented to predict the lifespan of bridges. Lifetime distribution functions (survivor functions) were used to model real bridges to predict their remaining life. Using the system reliability concept and lifetime distribution functions (survivor functions), a program called LIFETIME was developed. The survivor functions give the reliability of component at time t. The program was applied to an existing Colorado state highway bridge to predict the failure probability of the time-dependent system. The bridge was modeled as a system, with failure probability computed using time-dependent deteriorating models.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.165-173
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• 2008

Hybrid ad hoc networks are integrated networks referred to Home Networks, Telematics and Sensor networks can offer various services. Specially, in ad hoc network where each node is responsible for forwarding neighbor nodes' data packets, it should net only reduce the overall energy consumption but also balance individual battery power. Unbalanced energy usage will result in earlier node failure in overloaded nodes. it leads to network partitioning and reduces network lifetime. Therefore, this paper studied the routing protocol considering efficiency of energy. The suggested algorithm can predict the status of energy in each node using the energy prediction model. This can reduce the overload of establishing route path and balance individual battery power. The suggested algorithm can reduce power consumption as well as increase network lifetime.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.7-12
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• 2004

A lifetime prediction of holddown spring screw in nuclear fuel assembly was performed using fracture mechanics approach. The spring screw was designed such that it was capable of sustaining the loads imposed by the initial tensile preload and operational loads. In order to investigate the cause of failure and to predict the stress corrosion cracking life of the screw, a stress analysis of the top nozzle spring assembly was done using finite element analysis. The elastic-plastic finite element analysis showed that the local stresses at the critical regions of head-shank fillet and thread root significantly exceeded than the yield strength of the screw material, resulting in local plastic deformation. Normalized stress intensity factors for PWSCC life prediction was proposed. Primary water stress corrosion cracking life of the Inconel 600 screw was predicted by using integration of the Scott model and resulted in 1.78 years, which was fairly close to the actual service life of the holddown spring screw.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.111-111
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• 2004

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1173-1184
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• 2018

The lifetime of a lithium-ion battery is one of the most important issues of the energy storage system (ESS) because of its stable and reliable operation. In this paper, the lifetime management method of the lithium-ion battery for energy storage system is proposed. The lifetime of the lithium-ion battery varies, depending on the power usage, operation condition, and, especially the selected depth of discharge (DOD). The proposed method estimates the total lifetime of the lithium-ion battery by calculating the total transferable energy corresponding to the selected DOD and achievable cycle (ACC) data. It is also demonstrated that the battery model can obtain state of charge (SOC) corresponding to the ESS operation simultaneously. The simulation results are presented performing the proposed lifetime management method. Also, the total revenue and entire lifetime prediction of a lithium-ion battery of ESS are presented considering the DOD, operation and various condition for the nations of USA and Korea using the proposed method.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.61-66
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• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.44-50
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• 2021

Recently, enterprise storage systems that require large-capacity storage devices to accommodate big data have used large-capacity flash memory-based storage devices with high density compared to cost and size. This paper proposes a high-efficiency life prediction method with slope descent to maximize the life of flash memory media that directly affects the reliability and usability of large enterprise storage devices. To this end, this paper proposes the structure of a matrix for storing metadata for learning the frequency of defects and proposes a cost model using metadata. It also proposes a life expectancy prediction policy in exceptional situations when defects outside the learned range occur. Lastly, it was verified through simulation that a method proposed by this paper can maximize its life compared to a life prediction method based on the fixed number of times and the life prediction method based on the remaining ratio of spare blocks, which has been used to predict the life of flash memory.

• Korean Management Science Review
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• v.20 no.2
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.