• 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 Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.18-23
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• 2006

This paper presents an operator training simulator for power system restoration against massive blackout. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using power flow calculation methodology, and PSS/E data is used to set up the initial state for easy setting. The proposed simulator consists of three major components-a power flow(PF), a data conversion(CONV), and, a GUI module. The PF module calculates power flow, and then checks over-voltages of buses and overloads of lines. The CONV module composes a Y-Bus array and a database at each restoration action. The initial Y-Bus array is composed from PSS/E data. A user friendly GUI module is developed including a graphic editor and a built-in operation manual. The maximum processing time for one step operation is 15 seconds, which is adequate for training purpose.

• Transactions of the Korean hydrogen and new energy society
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• v.18 no.2
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• pp.151-156
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• 2007

Solar cell and nanofiltration membrane were utilized in a system of enzymatic hydrogen production through light-sensitized photoanode, which resembles photoelectrochemical(PEC) configuration. Solar cell uses no additional light energy to increase energy for electrons to reduce protons and for holes to oxidize water to oxygen, and nanofiltration membrane replaces a salt bridge successfully with increased ion transport capability. With this system configuration, optimized amount of enzyme(10.98 unit), and an anodized tubular $TiO_2$ electrode($5^{\circ}C$/1 hr in 0.5 wt% HF-$650^{\circ}C$/5 hr) hydrogen evolved at a rate of ca. $43\;{\mu}mol/(cm^2{\times}hr)$ in a cathodic compartment and oxygen generated at a rate of ca. $20\;{\mu}mol/(cm^2{\times}hr)$ in an anodic compartment. The stoichiometric evolution of gases indicated that water was splitted in the system.

• Transactions of the Korean hydrogen and new energy society
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• v.18 no.1
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• pp.40-45
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• 2007

Methylene blue(MB) was photocatalytically degraded with one-body photoanode and solar simulator to investigate the possible application to both environmental purification and photoelectrochemical cell for hydrogen production. Photoactive titanium dioxide was formed on both sides of Ti plate following steps such as rinsing-annealing-calcination or anodizing(20 V, 30 V)-annealing($350^{\circ}C$, $450^{\circ}C)$ after etching. The prepared titania plate($2cm{\times}2\;cm$, ca 1.6 mg $TiO_2$ on the basis of $1\;{\mu}m$ thickness) was used to degrade MB(10 ppm in 200 mL solution). The reaction tended to follow the Langmuir-Hinshelwood kinetics with zero order. Comparative experiments with Degussa P25 showed the same zero order kinetics when 2 mg of P25 had been used, while the first order kinetics when 200 mg used. This concludes the feasibility of the prepared titania plate as a material for the purification of low-level harmful organics and an electrode or a membrane for photoelectrochemical system for hydrogen production.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.181-183
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• 2003

This paper presents an operator training simulator for power system restoration against massive black-out. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using on line calculation methodology, and PSS/E data is used to define the initial situation. The proposed simulator consists of three major components - the Power flow(PF) module, data conversion(CONV) module and CUI subsystem. PF module calculates power flow, and then checks overvoltage of buses and overflow of lines. CONV module composes an Y-Bus array and a data base at each restoration action. The initial Y-Bus array is constructed from PSS/E data. The user friendly GUI module is developed including graphic editor and built-in operation manual. As a result, the maximum processing time for one step operation is 15 seconds, which is adequate for training purpose. Comparison with PSS/E simulation proves the accuracy and reliability of the training system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.120-125
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• 2020

Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.

• Journal of ILASS-Korea
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• v.24 no.3
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• pp.105-113
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• 2019

In this paper, a study of Urea-SCR System for Dosing Injector for responding to enhanced environmental regulations has been conducted. There is a limit to the experimental approach due to the structural characteristics of the injector. In order to overcome this problem, The analysis was performed assuming unsteady turbulent flow through computational fluid analysis and the internal flow characteristics of the injector were analyzed. By changing the nozzle shape of the injector, the performance factors of the swirl injector by shape were selected and compared. The design parameters were modified by changing the diameter of the nozzle at a constant ratio compared to the base model. Swirl coefficient, outlet mass flow, and sac volume were selected as performance parameters of the injector. The Conv. model to which the taper was applied showed the dominance in mass flow rate, discharge coefficient and swirl because of the smooth fluid flow by shape. Swirl coefficient, outlet mass flow, and sac volume were selected as performance parameters of the injector. As a result of the comparison coefficient derivation with those performance parameters for comparing the performance of the model-specific injector, the Conv-140 model with the nozzle diameter expanded by 140% showed the best value of the comparison coefficient.

• Journal of Drive and Control
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• v.19 no.4
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• pp.104-109
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• 2022

For autonomous driving research using AI, datasets collected from road environments play an important role. In other countries, various datasets such as CityScapes, A2D2, and BDD have already been released, but datasets suitable for the domestic road environment still need to be provided. This paper analyzed and verified the dataset reflecting the Korean driving environment. In order to verify the training dataset, the class imbalance was confirmed by comparing the number of pixels and instances of the dataset. A similar A2D2 dataset was trained with the same deep learning model, ConvNeXt, to compare and verify the constructed dataset. IoU was compared for the same class between two datasets with ConvNeXt and mIoU was compared. In this paper, it was confirmed that the collected dataset reflecting the driving environment of Korea is suitable for learning.