• 대한산업공학회지
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• 제27권3호
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• pp.267-273
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• 2001

While forecasting sales of a new product is very difficult, it is critical to market success. This is especially true when other products have a highly negative influence on the product because of competition effect. In this paper, we develop a choice-based competitive diffusion model and apply to the case where two digital mobile telecommunication services, that is, digital cellular and PCS services, compete. The basic premise is that demand patterns result from choice behavior, where customers choose a product to maximize their utility. In comparison with Bass-type competitive diffusion models, our model provides superior fitting and forecasting performance. The choice-based model is useful in that it enables the description of such competitive environments and provides the flexibility to include marketing mix variables such as price and advertising.

• Journal of Microbiology and Biotechnology
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• 제16권11호
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• pp.1720-1727
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• 2006

To set up a kinetic model that can provide a theoretical basis for developing a new mathematical model of the Cr(VI) biosorption column using brown seaweed Ecklonia biomass, a differential reactor system was used in this study. Based on the fact that the removal process followed a redox reaction between Cr(VI) and the biomass, with no dispersion effect in the differential reactor, a new mathematical model was proposed to describe the removal of Cr(VI) from a liquid stream passing through the differential reactor. The reduction model of Cr(VI) by the differential reactor was zero order with respect to influent Cr(IlI) concentration, and first order with respect to both the biomass and influent Cr(VI) concentrations. The developed model described well the dynamics of Cr(VI) in the effluent. In conclusion, the developed model may be used for the design and performance prediction of the biosorption column process for Cr(VI) detoxification.

• 소성∙가공
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• 제29권2호
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• Nuclear Engineering and Technology
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• 제17권2호
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• pp.116-128
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• 1985

본 논문에는 이산화 우라늄 소결체의 노내고밀화현상을 온도와 미세구조의 함수로 정화하게 예측할 수 있는 기계론적 이론 모형이 개발.기술되어있다. 이 모형은 $UO_2$ 소결체의 결정 입계내에서 공극(vacancy)이 생성 이동되고, 결정입계에서 공극이 소멸되는 현상을 고려하고 있으며, 이 과정에서 일어나는 고밀화의 크기가 핵분열율, 조사시간, $UO_2$ 소결체밀도, 기공 크기의 분포, 결정입크기 및 온도의 함수로 기술되어 있다. 본 모형의 기공 수축에 대한 결과식은 Assmann과 Stehle가 유도한 4개의 온도 영역에 대한 결과식과는 상이한 것으로서, 소결체의 모든 온도 영역에 직접 적용된다. 본 모형에 의한 노내고밀화 크기의 예측치는 실험자료와 아주 잘 일치하며, KEFDA 전산코드에 사용된 경험적 실험 연산식에 비하여 고밀화의 경향과 절대치를 보다 정확히 예측한다.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.1756-1768
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• 2021

The containment filtered venting system (CFVS) filters the atmosphere of the containment building and discharges a part of it to the outside environment to prevent containment overpressure during severe accidents. The Korean CFVS has a tank that filters fission products from the containment atmosphere by pool scrubbing, which is the primary decontamination process; however, prediction of its performance has been done based on researches conducted under mild conditions than those of severe accidents. Bubble behavior in a pool is a key parameter of pool scrubbing. Therefore, the bubble behavior in the pool was analyzed under various injection flow rates observed at the venturi nozzles used in the Korean CFVS using a wire-mesh sensor. Based on the experimental results, void fraction model was modified using the existing correlation, and a new bubble size prediction model was developed. The modified void fraction model agreed well with the obtained experimental data. However, the newly developed bubble size prediction model showed different results to those established in previous studies because the venturi nozzle diameter considered in this study was larger than those in previous studies. Therefore, this is the first model that reflects actual design of a venturi scrubbing nozzle.

• 대한원격탐사학회지
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• 제35권1호
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제26권1호
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• pp.23-48
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• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.

• 대한기계학회논문집A
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• 제20권1호
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• pp.146-153
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• 1996

A SCARA type direct drive robot which can be used in the assembly operation was designed and manufactured. Graphite fiber epoxy composite material was used in the fabrication of the robot arm structure in order to improve the speed of the robot arm with a high damping effect. For model-based control and sensitivity analysis of system parameters, the dynamic model of robot arm and drive servo amplifier parameters such as equivalent gains of PWM driver and velocity gains of servo system were estimated from frequency response tests. The complete dynamic model for overall robot system was used in the simulation of the open-loop control. The simulation results agreed reasonably well to the experimental results. The feedforward control using the dynamic models improved the trajectory tracking performance, decreasing the tracking error by factor of three compared with PID control. This study found that the inverse dynamic model of the robot arm including the drive servo system showed better performances than the case of arm dynamic model only.

• 한국항공우주학회지
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• 제49권11호
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• pp.961-961
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• 2021

• ETRI Journal
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• 제40권2호
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.