• Journal of Mechanical Science and Technology
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• v.20 no.4
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• pp.447-454
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• 2006

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.290-295
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• 2005

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• Journal of IKEEE
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• v.28 no.1
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• pp.6-13
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• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.173-179
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• 2003

In this paper, a real-time multibody vehicle dynamics and control model has been developed for a virtual reality intelligent vehicle simulator. The simulator consists of low PCs for a virtual reality visualization system, vehicle dynamics and control analysis system a control loading system, and a network monitoring system. Virtual environment is created by 3D Studio Max graphic tool and OpenGVS real-time rendering library. A real-time vehicle dynamics and control model consists of a control module based on the sliding mode control for adaptive cruise control and a real-time multibody vehicle dynamics module based on the subsystem synthesis method. To verify the real-time capability of the model, cut-in, cut-out simulations have been carried out.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.273-286
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• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.672-674
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• 1997

In this paper, we study the idle speed control of the spark ignition engine. Engine idle speed control is a difficult problem because of troublesome characteristics such as severe process nonlinearities, variable time delays, time-varying dynamics and unobservable internal system states and disturbances. We investigate the intelligent control algorithms such as neural network controller and fuzzy controller for 4-cylinder 4-stroke engine.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.1018-1023
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• 2010

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.49-58
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• 2006

In the respect of the environmental protection viewpoint, $CO_2$ may be one of the most attractive alternative refrigerants for an automotive air-conditioning system. For the development of control algorithm of a $CO_2$ automotive air-conditioning system, characteristics of a $CO_2$ refrigerant should be considered. The high-side pressure of a $CO_2$ system should be controlled in order to improve the system efficiency. In this study, dynamic physical models of a $CO_2$ system were developed and dynamic behaviors of the system were predicted by using these models. Control algorithms of a $CO_2$ system were also developed and the effectiveness of these algorithm was verified by using dynamic models.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.39-43
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• 2020

The Internet of Things plays a role as an important element technology of the 4th Industrial Revolution. This study is currently developing intelligent cars with IT technology, and is at a time when the development of intelligent cars is active and network data communication is possible. However, security solutions are needed as security is still at a weak stage, which can be threatened by intrusions into the network from outside. In this paper, in order to improve security of intelligent cars without causing security problems, we will apply blockchain technology, propose biometric authentication techniques using users' biometric information, and continue to study them in the future.