• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.6
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• pp.544-551
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• 2000

In this paper, 3-phase hybrid series active power filter for compensate current harmonics, voltage drop and unbalanced voltage in the network presented. The proposed system is implemented with a space vector modulation voltage source inverter and a high pass filter connected in parallel to the power system. Here the load is six-pulses thyristor rectifier. The phase angle detected in order to generation reference voltage at load terminal is synchronized with the positive sequence component of the unbalanced source by using symmetrical component transformation. The proposed system has an function harmonic isolation between source and load, voltage regulation, and unbalance compensation. Therefore, what the power system is improved quality, the source current is maintained as a nearly sinusoidal waveform and the load voltage is regulated with a rated voltage regardless of the source variation condition. To verify the validity of the proposed compensating system, the computer simulation and experiment are carried out.

• ETRI Journal
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• v.35 no.4
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• pp.655-665
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• 2013

Hybrid digital multimedia broadcasting (DMB) is a next-generation mobile TV system that combines broadcasting and wireless communication networks and can provide various high-quality multimedia services. However, if a system adheres to the current standard of transmitting the DMB content in the form of MPEG2-TS through wireless networks, it results in a burden on the network due to low transmission efficiency. The reasons for the low transmission efficiency are as follows. First, due to its constant bitrate characteristic, DMB MPEG2-TS includes a considerable amount of needless information, such as NULL packets and stuffing bytes. Second, due to the inflexibility of the Real-time Transport Protocol (RTP) standard, one cannot fully utilize the maximum transmission unit of the network when converting MPEG2-TS to RTP stream for transmission. This paper proposes a new transmission scheme that resolves these problems. Experiment results show that the proposed scheme improves data bitrate transmission efficiency by 8% to 36%, compared to the standard scheme, in the streaming of various real-DMB contents.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.133-141
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• 2005

The previous research has been mainly focused on individual technology of voice recognition, voice synthesis, translation, and bone transmission technical. Recently, commercial models have been produced using aforementioned technologies. In this research, a new automated translation support system concept has been proposed by combining established technology of bone transmission and wireless system. The proposed system has following three major components. First, the hybrid system consist of headset, bone transmission and other technologies will recognize user's voice. Second, computer recognized voice (using small server attached to the user) of the user will be converted into digital signal. Then it will be translated into other user's language by translation algorithm. Third, the translated language will be wirelessly transmitted to the other party. The transmitted signal will be converted into voice in the other party's computer using the hybrid system. This hybrid system will transmit the clear message regardless of the noise level in the environment or user's hearing ability. By using the network technology, communication between users can also be clearly transmitted despite the distance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.27-34
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• 2016

In the train system, railway vehicles are connected in a line. Therefore, this feature should be considered in composing network topology in a train system. Besides, inter-car communication should be distinguished from in-car communication. As for the inter-car communication, the hybrid topology was proposed to use rather than the conventional ring, star, daisy-chain, and bus topologies. In the hybrid topology, a number of cars are bound to be a group. Then star topology is used for the communication in a group and daisy-chain topology is used for the communication between groups. Hybrid topology takes the virtue of both star and daisy-chain topologies. Hence it maintains communication speed with reducing the number of connecting cables between cars. Therefore, it is important to choose the number of cars in a group to obtain higher performance. In this paper, we focus on the optimization of hybrid topology for railway cars. We first assume that the size of data and the frequency of data production for each car is identical. We also assume that the importance for the maximum number of cables to connect cars is variable as well as the importance of the communication speed. Separated weights are granted to both importance and we derive the optimum number of cars in a group for various number of cars and weights.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.809-812
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• 1999

The pendulum is a SIMO(Single-input multi-output) system that both angle of pendulum and position of cart controlled simultaneously by one actuator. In this paper, propose a hybrid neuro-controller to apply to pendulum system. We design the conventional optimal controller and the neural network as a identifier, which can identify the uncertainty of plant not modeled, respectively. Then we combine them into a novel controller, with a structure that the error between plant and identifier is added in conventional optimal control input Finally, the paper shows the validity of the proposed controller through computer simulations and experiments.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.468-475
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• 2008

This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin(${\Delta}V_n$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the ${\Delta}V_n$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.

• ETRI Journal
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• v.38 no.5
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• pp.838-846
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• 2016

The objective of digital broadcasting has evolved from providing a plain video service to offering a realistic visual experience. Technologies such as 3DTV and UHDTV have been suggested to achieve this new objective by providing an immersive and stereoscopic visual experience. However, owing to the high bandwidth requirements of such services, the broadcasting industry has faced a challenge to find a new transport mechanism for overcoming the bandwidth limitation. The standardization organizations, the Advanced Television Systems Committee, Digital Video Broadcasting, and Telecommunications Technology Association, have been working on the integration of broadcasting and a broadband network (IP) to resolve the bandwidth issue of realistic video services. This paper introduces a frame-level timeline synchronization and transport system target decoder model for providing a stable 3DTV service over a hybrid network. The experimental results indicate that the proposed technologies can be successfully adopted as a reference model in a broadcast-broadband hybrid 3DTV service and other IP-associated hybrid broadcasting services.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.429-436
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• 2002

In order to apply the artificial neural network to the practical application, it is needed to implement it with the hardware system. It is most promising to make it with the hybrid VLSI among various possible technologies. When we Implement a trained network into the hybrid neuro-chips, it is to be performed the process of the quantization on its neuron outputs and its weights. Unfortunately this process cause the network's outputs to be distorted from the original trained outputs. In this paper we analysed in detail the statistical characteristics of the distortion. The analysis implies that the network is to be trained using the normalized input patterns and finally into the solution with the small weights to reduce the distortion of the network's outputs. We performed the experiment on an application in the time series prediction area to investigate the effectiveness of the results of the analysis. The experiment showed that the network by our method has more smaller distortion compared with the regular network.