• Journal of Ocean Engineering and Technology
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• v.29 no.6
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• pp.488-498
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• 2015

This paper presents the dynamic model of an AUV called HW200 for underwater surveillance. The mathematical model of HW200 is briefly introduced, considering its shape. The maneuvering coefficients were initially estimated using empirical formulas and a database of vehicles with similar shapes. A motion simulator, based on Simulink of Mathworks, was developed to evaluate the mathematical model of the vehicle and to tune the maneuvering coefficients. The parameters were finely tuned by comparing the experimental results and simulated responses generated with the simulator by applying the same control inputs as the experiment. The velocity of HW200 in the tuning process was fixed at a constant forward speed of 1.83 m/s. Simulations with variable speed commands were conducted, and the results showed good consistency in the motion response, attitude, and velocity of the vehicle, which were similar to those of the experiment even under the speed variation. This paper also discusses the feasibility of its application to a model-based integrated navigation system (INS) using the auxiliary information on the velocities generated by the model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.3
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• pp.479-489
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• 1989

The $J_{k}$ integral method for determining mixed mode stress intensity factors separately in the cracked anisotropic plate is developed. Stress intensity factors are indirectly determined from the values of $J_{1}$ and $J_{2}$. The $J_{2}$ integral can be evaluated efficiently from a finite element solution, neglecting the contribution from the portion of the integration contour along the crack faces, by selecting the integration contour in the vicinity of the crack tip. Using functions of a complex variable, the complete relations between $J_{1}$, $J_{2}$ and $K_{I}$ , $K_{II}$ for anisotropic materials are derived conveniently by selecting narrow rectangular contours shrinking to the crack tip. Compared to the existing path independent integral methods, the present method does not involve calculating the auxiliary solution and hence numerical procedures become quite simple. Numerical results to various problems are given and demonstrate the accuracy, stability and versatility of the method.

• Journal of Advanced Navigation Technology
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• v.17 no.3
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• pp.332-345
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• 2013

In this paper, recently, it is described to the piezoelectric transformer technology develops, because it was have to favorable characteristics such as electromagnetic-noise free, compact size, higher efficiency, and superior power density, flux linkage, noiseless, etc. its resonance frequency was used to output waveform of a sine wave. A rotor speed identification method of induction motor based on the theory of flux model reference adaptive system(FMRAS). The estimator execute the rotor speed identification so that the vector control of the induction motor may be achieved. The improved auxiliary variable of the model are introduced to perform accurate rotor speed estimation. The control system is composed of the PI controller for speed control and the current controller using space voltage vector PWM techniuqe and DC-DC converter. High speed calculation and processing for vector control is carried out by digital signal one chip microprocessor. Validity of the proposed control method is verified through simulation and experimental results.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.91-99
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• 2022

The shipping industry is a service industry that operates its business by transporting cargo on ships and receiving freight. Therefore, large-scale capital investment is required for ship operation, and if the value of the ship is uncertain, the risk of shipping management increases. This study aims to identify the factors affecting changes in ship value and to analyze the importance of each variable. To achieve the goal, the factors affecting changes in ship value were identified and structured using the techniques of text mining and topic modeling, and classified into three main factors and 12 sub-factors. This study used AHP analysis to examine the relative importance of each factor. Results indicated that the main factor influencing the change in the vessel value was the shipping factor, followed by the investment factor and the environment factor. Other auxiliary factors that substantially affect the ship value include the volatility of the shipping market and of shipping freight.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.226-234
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• 2022

With advances in autonomous vehicles and networked control, there is a growing interest in the consensus control of a multi-agents system to control multi-agents with distributed control beyond the control of a single agent. Since consensus control is a distributed control, it is bound to have delay in a practical system. In addition, it is often difficult to have a very accurate mathematical model for a system. Even though a reinforcement learning (RL) method was developed to deal with these issues, it often experiences slow convergence in the presence of large uncertainties. Thus, we propose a slide RL which combines the sliding mode control with RL to be robust to the uncertainties. The structure of a sliding mode control is introduced to the action in RL while an auxiliary sliding variable is included in the state information. Numerical simulation results show that the slide RL provides comparable performance to the model-based consensus control in the presence of unknown time-varying delay and disturbance while outperforming existing state-of-the-art RL-based consensus algorithms.

• Korean Journal of School Psychology
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• v.18 no.3
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• pp.291-316
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• 2021

This study was conducted to identify how special education teachers perceive their school's organizational climate through latent profile analysis performed using Mplus, and determine whether there was a difference in the average teacher burnout rate between perception groups using three-step approaches. The participants were 312 special education teachers. The perception groups were identified as 'closed', 'laissez-faire', 'average', 'controlled', and 'autonomous.' The groups had different teacher burnout rates. The closed group had the highest rate, while the autonomous group had the lowest. This paper discusses the implications of these results for special education teacher burnout and school organizational climate, and suggests ideas for future studies.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.95-101
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• 2024

This paper aims to establish a reconfigurable flight simulation environment to conduct safety evaluation of various electric vertical take-off and landing (eVTOL) aircraft. Since the inceptor, aircraft dynamics model, and controller applied to each eVTOL aircraft are different, it was configured to be variable so that a simulation can be executed for each eVTOL aircraft. Test elements and performance indicators were set to perform safety evaluation of eVTOL aircraft. Ground auxiliary equipments were designed and implemented in a simulation environment according to test procedures for each test element. In addition, to analyze safety performance, a simulation flight data collection environment based on MATLAB/Simulink and a tool for safety performance analysis were implemented. Test flight and analysis were conducted in the implemented simulation environment in this paper. Finally, this study shows the environment was verified by confirming that it was performed normally.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.