• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.411-417
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• 2017

Environmental and growth factors such as light intensity, vapor pressure deficit, and leaf area index are important variables that can change the transpiration rate of plants. The objective of this study was to compare the transpiration rates estimated by modified Penman-Monteith model and artificial neural network. The transpiration rate of paprika (Capsicum annuum L. cv. Fiesta) was obtained by using the change in substrate weight measured by load cells. Radiation, temperature, relative humidity, and substrate weight were collected every min for 2 months. Since the transpiration rate cannot be accurately estimated with linear equations, a modified Penman-Monteith equation using compensated radiation (Shin et al., 2014) was used. On the other hand, ANN was applied to estimating the transpiration rate. For this purpose, an ANN composed of an input layer using radiation, temperature, relative humidity, leaf area index, and time as input factors and five hidden layers was constructed. The number of perceptons in each hidden layer was 512, which showed the highest accuracy. As a result of validation, $R^2$ values of the modified model and ANN were 0.82 and 0.94, respectively. Therefore, it is concluded that the ANN can estimate the transpiration rate more accurately than the modified model and can be applied to the efficient irrigation strategy in soilless cultures.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.175-180
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• 1998

In container crane system, the variation of cargo weight have effect on the travelling and sway control of load. For precise travelling and/or anti-sway control of crane system, the cargo weight should be measured and considered with control algorithm. But, and added attachment for measuring the cargo weight put restraint upon the control freedom for travelling and anti-sway. In this paper, we propose an estimation method for cargo weight in container crane system by using observation technique. First of all, we model the container crane system as a bilinear system and transform this model into linear system with external disturbance model. Second, we propose a generalized type - disturbance estimation observer and set a disturbance model, where, the cargo weight is related with the sway of load, and the sway is represented as a periodic external disturbance. Lastly, by using simulation we verify that the proposed algorithm of disturbance estimation observer is effective to estimate the cargo weight, and it will be used with anti-sway control algorithm.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.1-9
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• 2019

Recently, in the space market, there has been a rapid reduction of the launch price. The major reason is that a few commercial companies, especially SpaceX, began to enter into the space market about ten years ago, which has changed the space market from monopolization to competition, and accelerated the adoption of commercial efficiency in the technology and management. Also, the successful landing and recovery of a first stage in 2016 by SpaceX proved to be a prelude to opening a new era of reusable launch vehicles, and SpaceX declared the groundbreaking launch price through using the reusable launch vehicle. This study calculates the total launch cost required to put a certain satellite into the LEO, compares the launch cost in three cases with different payload weights, and reviews the impacts of the payload on the cost effectiveness of a reusable vehicle. The total launch cost is divided into 6 subsections cost, namely development cost, production cost, refurbishment cost, operation cost, fixed-cost of factory and launch site, and insurance cost. The cost estimation relationships used in the calculation are taken from the commonly proven cost models such as TRANSCOST.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.6
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• pp.373-382
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• 2013

The weight of floating offshore plant, such as an FPSO(Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, is important for estimating the amount of production material and for determining the production method. Furthermore, the weight is a factor which affects in the building cost and production time of the floating offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer's experience. To solve this problem, a simplified model for the weight estimation of the floating offshore plant using the statistical method was proposed in this study. To do this, various data for estimating the weight of the floating offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to generate the simplified model for the weight estimation. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of an FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore plant at the early design stage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.665-675
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• 2008

The objective of this paper is to provide an approximate cost estimating model for tunnel that can be utilized both in quick construction cost estimating for design alternatives, and in evaluating efficiently the cost effects according to the environmental changes during design and construction stage. To meet this requirement, this study analyzes critical cost factors influencing tunnel construction costs. The cost factors include 7 elements such as rock drilling method, advancing method, type of detonator, loader capacity, unit weight and soil volume change factor, length of tunnel. This paper investigates the cost variance according to the change of the cost factors. The result is expected to be used in formulating approximate tunnel cost estimating model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.4
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• pp.368-376
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• 2015

This paper is the study on the establishment of design model to improve design efficiency using modified weight estimation equation on the initial design stage for development of a helicopter. The methodology to extract coefficients of the weight estimation equation was proposed through the influence investigation for the weight of components and the parameter study and sensitivity analysis for design variables such as the rotor disk loading, the number of blade and the aspect ratio of blade were also performed. As a result of study, the relation of parameters and degree of sensitivity of parameters on helicopter design are considerable points for optimization of helicopter characteristics, and it is necessary for designer to consider the complex relation of main parameters.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.129-138
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• 2012

A MDO study of a midium-sized solar powered High Altitude Long Endurance (HALE) UAV has been performed, focused on energy balance. In the MDO process, Vortex Lattice Method(VLM) is employed for the aerodynamic modeling of the vehicle, of which structural weight is estimated with the modeling proposed by Cruz. Tail volume ratios have been set as constants, while the location of tail surfaces is determined from longitudinal static stability criterion. By balancing the available energy from solar cells, battery, and altitude, with the energy-requirement of the vehicle, the possibility of continuous flight over 24-hours has been investigated. The solar radiation level is set as that of summer at the latitude of $36^{\circ}$ north. During the daytime, the aircraft climbs using solar energy, accumulating potential energy, which supplements energy balance during the night. Optimizations have been sought in size of the vehicle, its weight distribution, and flight strategy.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.387-393
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• 2016

To perform realistic air traffic control (ATC) simulation in various air traffic situations, an aircraft dynamic model that is accurate and efficient is required. In this research, an improved five degree of freedom (5-DOF) dynamic model with feedback control and guidance law is developed, which utilizes selected performance data and operational specifications from the base of aircraft data (BADA) and estimations using aircraft design techniques to improve the simulation fidelity. In addition, takeoff weight is estimated based on the aircraft type and flight plan to improve simulation accuracy. The dynamic model is validated by comparing the simulation results with recorded flight trajectories. An ATC simulation system using this 5-DOF model can be used for various ATC related research.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.10-18
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• 2024

Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.