• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.66-73
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• 2007

일반 투자안의 의사결정에서와 마찬가지로 산업설비의 경제성 분석에서도 가장 중요한 결정 요소 중의 하나가 설비의 생존곡선 추정이다. 설비의 자산 가치가 감소하는 원인은 여러 가지가 있으나, 여러 원인 중 물리적 훼손이 과거의 산업설비에서는 가장 중요한 원인이었으므로 기존의 생존모형 분석에서는 lows 생존곡선을 이용하여 설비의 생존곡선을 추정하였다. 그러나 새로운 기술상의 변화로 인한 첨단 생산시스템의 설비교체 분석 시에는 적합지 않다. 따라서, 본 연구에서 제안된 혼합형 Weibull 함수를 이용하여 설비의 폐기 형태를 추정함으로써 설비들의 실제적인 생존곡선을 정확하게 파악할 수 있다.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of Korean Society of Forest Science
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• v.94 no.5 s.162
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• pp.321-329
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• 2005

Based on the data representing four typical Korean pine forest types, the age structure, DBH distribution, species composition, and forking rule were systemically analyzed for old-growth Korean pine forest in Liangshui Nature Reserve, northeast China. The age structure of Korean pine trees was strongly uneven-aged with one dominated peak following normal distribution, and age of trees varied from 100 to 180 years within a stand. The DBH and height differences in same age class (20 years) varied from 28 cm~64 cm and 5 to 20 m, respectively. Many conifer and hard wood species, such as spruce, fir, costata birch, basswood, oak, and elm, were mixed with dominated trees of Korean pine. The canopy of the old-growth Korean pine forest can be divided into two layers, and differences of mean age and height between Layer I and Layer II were ranged 80~150 years and 7~13 m, respectively. The Weibull function was used to model the diameter distribution and performed well to describe size-class distribution either with a single peak in over-story canopy and inverse J-shape in under-story canopy for old-growth Korean pine stands. The forking height of Korean pine trees ranged from 16m to 24 m (mean 19.4 m) and tree age about 120 to 160 years old. The results will provide a scientific basis to protect and recover the ecosystem of natural old-growth Korean pine and also provide the model in management of Korean pine plantation.