• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.10-18
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• 2003

In this paper, we proposed a learning algorithm for the ADALINE network. The proposed algorithm exploits fuzzy system for automatic tuning of the weight parameters of the ADALINE network. The inputs of the fuzzy system are error and change of error, and the output is the weight variation. We used different scaling factor for each weights. In order to verify the effectiveness of the proposed algorithm, we peformed the simulation and experimentation for the cases of the noise cancellation and the inverted pendulum control. The results show that the proposed algorithm does not need the learning rate and improves 4he performance compared to the Widrow-Hoff delta rule for ADALINE.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.147-149
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• 2007

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.168.1-168
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• 2001

This article describes a novel model-based compensating neural network (NN) model developed to be used in our active binocular head controller, which addresses both the kinematics and dynamics aspects in trying to precisely track a moving object of interest to keep it in view. The compensating NN model is constructed using two classes of self-tuning neural models: namely Neural Gas (NG) algorithm and SoftMax function networks. The resultant servo controller is shown to be able to handle the tracking problem with a minimum knowledge of the dynamic aspects of the system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.363-369
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• 1999

In these days, there are a lot of studies in the port automation, for example, unmanned container trasporter, unmanned gantry crain, and automatic terminal operation systems and so on. In terms of loading and unloading equipments. we can consider container transporter. This paper describes the automatic control for the UCT(unmanned container transporter), especially steering control systems. UCT is now operated on ECT port in Netherland and tested on PSA ports in Singapore. So we present a design on the controller using neural network PID(NNPID) controller to control the steering system and we use the neural network self-tuner to tune the PID parameters. The computer simulations show that our proposed controller has better performances than those of the other.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• JSTS:Journal of Semiconductor Technology and Science
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• v.4 no.2
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• pp.83-87
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• 2004

Phase noise performance and current consumption of Radio Frequency (RF) Voltage-Controlled Oscillator (VCO) are largely dependent on the Quality (Q) factor of inductor-capacitor (LC) tank. Because the Q-factor of LC tank is determined by on-chip spiral inductor, we designed, analyzed, and modeled on-chip differential inductor to enhance differential Q-factor, reduce current consumption and save silicon area. The simulated inductance is 3.3 nH and Q-factor is 15 at 2 GHz. Self-resonance frequency is as high as 13 GHz. To verify its use to RF applications, we designed 2 GHz differential LC VCO. The measurement result of phase noise is -112 dBc/Hz at an offset frequency of 100 kHz from a 2GHz carrier frequency. Tuning range is about 500 MHz (25%), and current consumption varies from 5mA to 8.4 mA using bias control technique. Implemented in $0.35-{\mu}m$ SiGe BiCMOS technology, the VCO occupies $400\;um{\times}800\;um$ of silicon area.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.133-140
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• 2007

Currently, as the requirement for high quality Internet access from anywhere at anytime is consistently increasing, the interconnection of pure ad hoc networks to fixed IP networks becomes increasingly important. Such integrated network, referred to as hybrid ad hoc networks, can be extended to many applications, including Sensor Networks, Home Networks, Telematics, and so on. We focus on some data communication problems of hybrid ad hoc networks, such as broadcasting and routing. In particular. power failure of mobile terminals is the most important factor since it affects the overall network lifetime. We propose an energy-efficient routing protocol based on clustering for hybrid ad hoc networks. By applying the index-based data broadcasting and selective tuning methods, the infra system performs the major operations related to clustering and routing on behalf of ad hoc nodes. The proposed scheme reduces power consumption as well as the cost of path discovery and maintenance, and the delay required to configure the route.

• ETRI Journal
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• v.20 no.4
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• pp.301-326
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• 1998

In this paper, we have modeled and evaluated a new parallel processing system called Scalable Parallel computer Architecture based on Xbar (SPAX) for commercial applications. SMP systems are widely used as servers for commercial applications; however, they have very limited scalability. SPAX cost-effectively overcomes the SMP limitation by providing both scalability and application portability. To investigate whether the new architecture satisfies the requirements of commercial applications, we have built a system model and a workload model. The results of the simulation study show that the I/O subsystem becomes the major bottleneck. We found that SPAX can still meet the I/O requirement of the OLTP workload as it supports flexible I/O subsystem. We also investigated what will be the next most important bottleneck in SPAX and how to remove it. We found that the newly developed system network called Xcent-Net will not be a bottleneck in the I/O data path. We also show the optimal configuration that is to be considered for system tuning.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2432-2434
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.