• Journal of Internet Computing and Services
• /
• v.17 no.4
• /
• pp.43-50
• /
• 2016

In this paper, we propose the protocol of Ethernet that will receive a popular interesting in the automotive intelligent network, it also attempts to implementation and verification through simulation and experiments to propose a fault tolerance algorithm when the data transfer on it. It has proven the usefulness of the system in order to apply toward an existing automotive communication system. In the case of actual real-time data for automotive industry, we generated a randomly-generated data which is the set of payload into a standard format to complete the experiment. Among the implemented existing algorithms performance, we confirmed the effectiveness of all range from a single data to mixed (Hybrid-type) data, to verify the proposed algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.5
• /
• pp.1-9
• /
• 2007

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the coner and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Mechanical Science and Technology
• /
• v.20 no.4
• /
• pp.447-454
• /
• 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
• /
• 2005.06a
• /
• pp.290-295
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.4
• /
• pp.220-227
• /
• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.9
• /
• pp.3151-3168
• /
• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.692-697
• /
• 2011

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid mobile robot that can dock with the other robot and transforms between wheeled robot and legged robot is proposed. The hybrid mobile robot platform has docking device with a peg and a cup module. In addition, the robot is possible to walk and drive according to condition of the road. A navigation algorithm of the hybrid mobile robot is proposed to improve the mobility of robots using docking algorithm based on image processing on the broken road and uneven terrain. The proposed method recognizes road condition through PSD sensor attached in front and bottom of the robot and selects an appropriate navigation method according to terrain surface. The proposed docking and navigation methods are verified through experiments using hybrid mobile robots.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2006.05a
• /
• pp.321-326
• /
• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the KIEE Conference
• /
• 2006.07b
• /
• pp.737-738
• /
• 2006

This paper is proposed hybrid artificial intelligent controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.323-335
• /
• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.