• Computers and Concrete
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• v.32 no.4
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• pp.393-398
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• 2023

This paper provides the free vibrations of chiral carbon nanotubes. The governing equations of Flügge theory is considered for vibration frequencies of chiral single walled carbon nanotubes. The solution of frequency equation is obtained from a novel model for better representation of stubby and short vibration characteristics of chiral tubes with clamped-clamped and clamped-simply supported end conditions. For the harmonic response of this tube, the model displacement function is adopted. The variational approach Rayleigh-Ritz method with kinetic and strain energies are used. The Lagragian function is differentiated with respect to unknown functions. The frequency equation is written in compact form to solve with MATLAB software. The frequencies of chiral SWCNTs for first ten aspect ratios as small level are investigated. The results shown as for decreasing the aspect rations, the frequencies are increases. The presented results of this model are verified with experimental and numerical results, which found as an excellent agreement.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1255-1266
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults. SUBJECTS/METHODS: Participants aged over 65 years from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB^® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output. RESULTS: Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat. CONCLUSIONS: In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.3
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• pp.432-441
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• 2016

Solar energy is the ideal power choice for long-endurance stratospheric airships. The output performance of solar array on stratospheric airship is affected by several major factors: flying latitude, flight date, airship's attitude and the temperature of solar cell, but the research on the effect of these factors on output performance is rare. This paper establishes a new simplified analytical model with thermal effects to analyze the output performance of the solar array. This model consisting of the geometric model of stratospheric airship, solar radiation model and incident solar radiation model is developed using MATLAB computer program. Based on this model, the effects of the major factors on the output performance of the solar array are investigated expediently and easily. In the course of the research, the output power of solar array is calculated for five airship's latitudes of $0^{\circ}$, $15^{\circ}$, $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$, four special dates and different attitudes of five pitch angles and four yaw angles. The effect of these factors on output performance is discussed in detail. The results are helpful for solving the energy problem of the long endurance airship and planning the airline.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.28-38
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• 2012

The characteristics of vibration and noise of a compressor used for electric appliances have significant influence on the quality of the products. For improvement on the quality of electric appliances, investigations for understanding the dynamic behaviour of the compressor are essential. Since Virtual Lab for the dynamics model and MAXWELL for the electromagnetics model are separate software programs with no interface, the joint simulation of the models could not be performed. This study suggests a way to develop the compressor model capable of the joint simulation with MATLAB/SIMULINK linking a flexible multi-body dynamics model, a torque model, and an electricity control model. The compressor model is found to be able to perform I/O data transfer among the sub-models and joint simulation. The simulation results of the flexible body and rigid body dynamics models were compared to check availability of the joint simulation system. In addition, the simulated vibration and driving torque of the compressor mechanisms were compared with measurements. Through the simulations, the influence of springs and LDT on the dynamic behaviour of the compressor was examined. This study examines the influence of the dynamic behaviour of the compressor mechanisms through joint simulation of the flexible multi-body dynamics model and electromagnetic circuit allows analysis.

• Journal of Korean Society of Transportation
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• v.18 no.2
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• pp.73-89
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• 2000

This Paper documents the development of a cycle free based, coordinated dynamic signal timing model for minimizing queue lengths using Genetic A1gorithm. The model was embodied using MAT-LAB, the language of technical computing. A special feature of this model is its ability to manage queue lengths of turning movements at the start of green times. The model produces a cycle-free based signal timing(cycles and green times) for each intersection to minimize queue lengths of turning movements on the cycle basis. Concurrently, appropriate offsets could be accomplished by applying cycle-free based signal timings for respective intersections. The model was applied to an example network which consists of three intersections. The result shows that the model produces superior signal timings to the existing signal timing model in terms of managing queue lengths of turning movements.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.81-98
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• 2002

This paper documents the development of a bi-objective(minimizing delays and Queue lengths) cycle-free signal timing length model using Genetic Algorithm. The model was embodied using MATLAB. the language of technical computing. A special feature of this model is its ability to concurrently manage delays and queue lengths of turning movement concurrently. The model produces a cycle-free signal timing(cycles and green times) for each intersection on the cycle basis. Appropriate offsets could be also accomplished by applying cycle-free based signal timings for respective intersections. The model was applied to an example network which consists of four intersections. The result shows that the model produces superior signal timings to the existing signal timing model in terms of managing delays and queue lengths of turning movements.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.5
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• pp.376-382
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• 2014

This paper presents a method to enable a UAV in autonomous flight along a desired path to follow it backwards when a strong headwind prevents the vehicle from proceeding forward. The main purpose of the reverse path following in this study is to return to a mission quickly when the wind becomes weaker. When the nonlinear path following guidance law is used, there are two reference points available in the path following. One of the two points is selected considering a flight direction for calculating a straight-line distance(L) from the vehicle to the point for the path following. An initial heading angle with respect to the wind direction determines whether the reverse path following is feasible or not at the time of the wind is generated. The result of the proposed method based on kinematic model in this study is verified through simulations implemented in Matlab.