• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.73-76
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• 2001

It is well-known that the convergence rate gets worse when an input signal to an adaptive filter is correlated. In this paper we propose a new adaptive filtering algorithm that makes the convergence rate highly improved even for highly correlated input signals. By introducing an orthogonal constraint between successive input signal vectors, we overcome the slow convergence problem caused by the correlated input signal. Simulation results show that the proposed algorithm yields highly improved convergence speed and excellent tracking capability under both time-invariant and time varying environments, while keeping both computation and implementation simple.

• Communications of the Korean Mathematical Society
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• v.13 no.3
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• pp.603-627
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• 1998

The leaky bucket scheme is a promising method that regulates input traffics for preventive congestion control. In the ATM network, the input traffics are bursty and transmitted at high-speed. In order to get the low loss probability for bursty input traffics, it is known that the leaky bucket scheme with static leaky rate requires larger data buffer and token pool size. This causes the increase of the mean waiting time for an input traffic to pass the policing function, which would be inappropriate for real time traffics such as voice and video. We present the leaky bucket scheme with dynamic leaky rate in which the token generation period changes according to buffer occupancy. In the leaky bucket scheme with dynamic leaky rate, the cell loss probability and the mean waiting time are reduced in comparison with the leaky bucket scheme with static leaky rate. We analyze the performance of the proposed leaky bucket scheme in discrete-time case by assuming arrival process to be Markov-modulated Bernoulli process (MMBP).

• Language and Information
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• v.7 no.1
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• pp.87-99
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• 2003

This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach.

• Environmental Engineering Research
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• v.16 no.1
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• pp.19-27
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• 2011

This paper introduces an approach to identify the total energy consumption with subsequent $CO_2$ emissions, for both industrial and non-industrial sectors. Statistical data for 2005 were compiled in a national account system to construct an energy input-output table for investigating the influence between energy demand and supply activities. The methodological approach was applied to South Korea. Twelve types of energy and fifteen industrial and non-industrial sectors are formed as the compartments of the input-output table. The results provided quantitative details of the energy consumption and identified the significant contributions from each sector. An impact analysis on the $CO_2$ emissions for the demand side was also conducted for comparison with the supply side.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1222-1227
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• 1990

The problem of identification of continuous systems is considered when both the discrete input and output measurements are contaminated by white noises. Using a predesigned digital low-pass filter, a discrete-time estimation model is constructed easily without direct approximations of system signal derivatives from sampled data. If the pass-band of the filter is designed so that it includes the main frequencies of both the system input and output signals in some range, the noise effects are sufficiently reduced, accurate estimates can be obtained by least squares(LS) algorithm in the presence of low measurement noises. Two classes of filters(infinite impulse response(IIR) filter and finite impulse response(FIR) filter) are employed. The former requires less computational burden and memory than the latter while the latter is suitable for the bias compensated least squares(BCLS) method, which compensates the bias of the LS estimate by the estimates of the input-output noise variances and thus yields unbiased estimates in the presence of high noises.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.133-141
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• 1994

Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.90-94
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• 2008

It is well-blown that the convergence rate gets worse when an input signal to an adaptive filter is correlated. In this paper we propose a new adaptive filtering algorithm that makes the convergence rate much improved even for highly correlated input signals. By introducing an orthogonal constraint between successive input signal vectors we overcome the slow convergence problem of the LMS algorithm with the correlated input signal. Simulation results show that the proposed algerian yields fast convergence speed and excellent tracking capability under both time-invariant and time-varying environments, while keeping both computation and implementation simple.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.89-91
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• 2007

In this paper, a new approach for a sampled-data representation of nonlinear system that has time-delayed multi-input is proposed. That is largely devoid of illconditioning and is suitable for any nonlinear problem. The new scheme is applied to nonlinear systems with two or three inputs; and then the delayed multi-input general equation is derived. The method is based on thematrix exponential theory. Itdoes not require excessive computational resources and lends itself to a short and robust piece of software that can be easily inserted into large simulation packages. A performance of the proposed method is evaluated using a nonlinear system with time-delay: maneuvering an automobile.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.209-216
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• 2000

Recently, Wearable Computers have been applied to medical equipments, inspection system, military and various fields of industries. To support the various application of wearable computer, many researches into the input device for wearable computer have been executed. This paper describes the glove-like hand input system for wearable computer. the characteristics of sensed values, and coupling effects between each sensor. Using these characteristics and coupling effects, the general relation between flexion angles of joints and the values from sensors are proposed as exponential functions. Also, the error range of sensed values is proposed and the glove-like hand input system is calibrated as well by the experiments.