• Journal of Drive and Control
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• v.16 no.3
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• pp.67-74
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• 2019

The automotive market has recently been investing much time and costs in improving existing technologies such as ABS (Anti-lock Braking System) and TCS (Traction Control System) and developing new technologies. Additionally, various methods have been applied and developed to reduce this. Among them, the development method using the simulation has been mainly used and developed. In this paper, we have studied a method to develop SILS (Software In the Loop Simulation) for TCS which can test various environment variables under the same conditions. We modeled hardware (vehicle engine and ABS module) and software (control logic) of TCS using MATLAB/Simulink and Carsim. Simulation was performed on the climate, road surface, driving course, etc. to verify the TCS logic. By using SILS to develop TCS control logic and controller, it is possible to verify before production and reduce the development period, manpower and investment costs.

• Journal of Drive and Control
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• v.15 no.4
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• pp.81-90
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• 2018

Indoor construction site such as building demolition sites, tunnel, vinyl house, and cattle shed are subject to various risk factors such as falling stones, soot and bad odors. However, most of the mini excavators have no cabin that can protect the driver from such risk factors. Therefore, researches on remote control technology of construction equipment are actively conducted as a method for protecting the driver from the risk factors occurring in the working environment. For effective remote control, it is necessary to be able to control the travelling and work using a portable small transmitter. However, due to the limitation of the size of the transmitter, complex operation control is required to control two or more actuators with a single joystick. Also, it is essential to check how remote control characteristics change in various environments such as distance, signal strength, obstacle. Therefore, in this study, an algorithm that can control two actuators simultaneously with a single joystick signal was developed, and a communication method suitable for indoor and outdoor mini construction equipment by analyzing experimentally how the remote control characteristics vary according to various work environments and telecommunication methods proposed.

• Journal of Drive and Control
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• v.16 no.2
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• pp.80-90
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• 2019

Fuel cell hybrid electric vehicle is an attractive solution to reduce pollutants, such as noise and carbon dioxide emission. This study presents an approach for energy management and control algorithm based on energetic macroscopic representation for a fuel cell hybrid electric vehicle that is powered by proton exchange membrane fuel cell, battery and supercapacitor. First, the detailed model of the fuel cell hybrid electric vehicle, including fuel cell, battery, supercapacitor, DC-DC converters and powertrain system, are built on the energetic macroscopic representation. Next, the power management strategy was applied to manage the energy among the three power sources. Moreover, the control scheme that was based on back-stepping sliding mode control and inversed-model control techniques were deduced. Simulation tests that used a worldwide harmonized light vehicle test procedure standard driving cycle showed the effectiveness of the proposed control method.

• Progress in Superconductivity and Cryogenics
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• v.22 no.4
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• pp.40-44
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• 2020

This paper describes the real operation of 2 kW class reverse-Brayton refrigeration system with neon as a working fluid. The refrigeration cycle is designed with operating pressure of 0.5 and 1.0 MPa at low and high pressure side, respectively. Compressor package consists of several helium scroll compressors witch are originally used for driving GM cryocooler. Three segments of plate heat exchanger are adopted to cover the wide temperature range and the refrigeration power is produced by turbo expander. The developed refrigeration system is successfully operated at its target temperature of 77 K. In experiments, all parameters such as pressure, temperature, mass flow rate and valve opening are measured to investigate characteristics during cool-down process and normal state. The difference between design and real operation is discussed with measured experimental data. At normal state of 77 K operation, the developed reverse-Brayton refrigeration system shows 1.83 kW at 68.2 K of cold-end temperature.

• Journal of Drive and Control
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• v.17 no.4
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• pp.113-124
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• 2020

Most of the work of construction equipment and agricultural machinery is done in off-road conditions. Autonomous driving required in these conditions uses GPS sensors, and PID controllers to control their speed and position. The hydrostatic steering, which is composed of a PSU, hydraulic hoses, and cylinders, rather than a mechanical coupling is used in these equipments. The PSU plays a key role in hydrostatic steering. Precise control of the position under various conditions requires detailed behavioral analysis of the basic components and operation. Two Gerotor PSU is now a commonly used safer option. The components of the PSU can be divided into mechanical and hydraulic actuating elements by its behavior. Since the system is combined by mechanical and hydraulic elements, the modelings are performed using Amesim, which is one of the most effective for the multi-domain dynamic system analysis. To confirm the validity of the model, input torque and pressures are checked with varying steering speed. The opening and the steering speed of normal and newly designed control valve set is investigated with the effect of centering spring force and friction. Finally, simulation results with fully detailed model with two gerotors are analyzed and compared with simple model.

• Journal of Drive and Control
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• v.18 no.4
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.1
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• pp.55-60
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• 2022

This study conducted the measurements of air flow rate for blower systems with experiment and numerical. A new airflow rate test method is suggested, with which it is possible to accurate measurements and calculate the air flow rate for blower systems. The blower(axial fan) is an industrial fluid machine device that supplies a large amount of air by driving an impeller with an electric motor, and it is widely used throughout the industry such as steel, power plant, chemical, semiconductor, LC D, food, and cement. The airflow from the blower is for exchanging the heat in the cooling unit or heat exchanger. The temperature of coolants and hydraulic oil primarily depends on the amount of airflow rate through the cooling package so its accurate estimation is very important. Moreover, it required a larger investment in time and cost since it could not be executed until the system is actually made. Therefore, this research is intended to examine the phenomenon of air flow pattern when testing air flow rate, suggested new test method, and show the result of the validation test.

• Journal of Drive and Control
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• v.19 no.1
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• pp.69-75
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• 2022

This study aimed to develop solutions for the intermittent performance deterioration of self-propelled spinach harvesters through analysis model. The study was conducted in the following manner. First, changes in performance deterioration and surplus flow, which result from oil temperature changes, were analyzed by simulating actual sequential harvesting movements, which involve driving with actuators operated simultaneously, by analysis model developed in a previous study. Second, fundamental solutions for surplus flow problems were presented. Third, the solutions were applied to a virtual environment to present their practicality and quantitative effects. The two solutions based on the study results were as follows. First, a closed center-type directional control valve was applied to the hydraulic circuit. Second, an unloading system was set up through an on-off solenoid valve.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Journal of Drive and Control
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• v.20 no.4
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• pp.140-149
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• 2023

Wire harness is a component for efficient control when electronic parts are required such as construction machinery and electric vehicles. With emerging issues such as autonomous driving and automation in construction, a wire harness composed of multiple cables has become an essential part because more electronic parts are required. However, when a wire harness failure occurs, systems can be stopped, accidents can occur, and economic damage can be significant. Therefore, in this paper, we developed a digital fault screening system that could easily and quickly diagnose faults in the wire harness. The principle of the developed system was to sequentially send pulse signals to the wire harness and use returned signals to perform fault detection. As a result of diagnosing faults using the developed failure detection system, a detection accuracy of 99.9 % was confirmed through the experiments.