• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• Computers and Concrete
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• v.5 no.6
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• pp.573-597
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• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

• Korean Journal of Microbiology
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• v.28 no.2
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• pp.134-144
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• 1990

In order to examine that what kind of system correlated with cadmium detoxification mechanism in Klebsiella aerogenes ATCC 10031, we tried to investigate the effect of phosphate upon the detoxification and also elucidate whether the cadmium phosphate and/or polymeric Cd-Pi complex is formed actually in cell or not. As the results, it was shown that growing pattern had long lag adaptive phase of 12 hr to 24 hr, at the concentrations of 0.02 mM and 0.08 mM cadmium, respectively. Cadmium was accumulated more highly in the fraction of cell wall and membrane than in those of cytoplasm. In case of phosphate starving cells added cadmium, inorganic polyphosphate system was primarily correlated with Cd-detoxification during the lag phase for the accommodation to cadmium, on the other hand, Cd:Sulfide complex system secondarily correlated it during the stationary phase. These results implied that polyphosphate system and Cd:sulfide complex system, these two systems were operated compensatively each other. Considering the results obsdrved with EM and examined tha changes of sulfide and polyphosphate amount, it was reflected that Cd:S complex was located at the cell surface. In the results of $in-vivo^{31}$P NMR spectra in the cells with cadmium pressure, several phosphate signals arose newly from the polyphosphate region with moving chemical shift of it. This phinomenon strongly implied the actual existence of Dd:Pi comples and /or Cd:poly-P complex in the cell and also the cellular compartmentalization of cadmium detoxifying mechanism.

• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.1
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• pp.54-60
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• 2006

In this paper, we describe a bio-fluidic device for adaptive sample pretreatment, in order to optimize the conditions under which absorbance assays can be conducted. This device can be successfully applied to the measurement of Escherichia coli (E. coli) concentrations using adaptive dilution, with which the dilution ratio can be adjusted during the dilution. Although many attempts have been previously made to miniaturize complex biochemical analyses at the chip scale, very few sample pretreatment processes have actually been miniaturized or automated at this point. Due to the lack of currently available on-chip pretreatments, analytical instruments tend to suffer from a limited range of analysis. This occasionally hinders the direct and quantitative analysis of specific analyses obtained from real samples. In order to overcome these issues, we exploit two novel strategies: dilution with a programmable ratio, and to-and-fro mixing. The bio-fluidic device consists of a rectangular chamber constructed of poly(dimethylsiloxane) (PDMS). This chamber has four openings, an inlet, an outlet, an air control, and an air vent. Each of the dilution cycles is comprised of four steps: detection, liquid drain, buffer injection, and to-and-fro mixing. When using adaptive sample pretreatment, the range in which E. coli concentrations can be measured is broadened, to an optical density (O.D.) range of $0.3{\sim}30$. This device may prove useful in the on-line monitoring of cell concentrations, in both fermenter and aqueous environments.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.1
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• pp.1-7
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• 2000

In this paper, we proposed a complex multirate filter bank(CMRFB) based adaptive notch filtering technique to solve the co-existence narrowband interference problem of DS-CDMA and TDMA systems. We have discussed error performance of co-existence of DS-CDMA and TDMA system by using complex multirate filter bank in land mobile channel and computer simulation results show that the proposed scheme can eliminate the narrowband interference(TDMA signals) effectively.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.2
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• pp.138-143
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• 1999

A new control method for the precision robust position control of a brushless DC(BLDC) motor for direct drive m motor(BLDDM) system using the asymptotically stable adaptive load torque observer is presented. A precision position c control is obtained for the BLDD motor system appro성mately linearized using the fieldlongrightarroworientation method. Many of t these motor systems have BLDD motor to obtain no backlashes. On the other hand, it has disadvantages such as the h high cost and more complex controller caused by the nonlinear characteristics. And the load torque disturbance is d directly affected to a motor shaft. To r밍ect this problem, stability analysis is calTied out using Lyapunov stability t theorem. Using this results, the stability is proved and load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent CUlTent having the fast response.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.720-723
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• 2005

Ship motion is a complex controlled process with several hydrodynamic parameters that vary in wide ranges with respect to ship load condition, speed and surrounding conditions (such as wind, current, tide, etc.). Therefore, to effectively control ships in a designed track is always an important task for ship masters. This paper presents an effective adaptive autopilot ships that ensure the optimal accuracy, economy and stability characteristics. The PID control methodology is modified and parameters of a PID controller is designed to satisfy conditions for an optimal objective function that comprised by heading error, resistance and drift during changing course, and loss of surge velocity or fuel consumption. Designing of the controller for course changing process is based on the Model Reference Adaptive System (MRAS) control theory, while as designing of the automatic course keeping process is based on the Self Tuning Regulator (STR) control theory. Simulation (using MATLAB software) in various disturbance conditions shows that in comparison with conventional PID autopilots, the designed autopilot has several notable advantages: higher course turning speed, lower swing of ship bow even in strong waves and winds, high accuracy of course keeping, shorter time of rudder actions smaller times of changing rudder direction.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.4-9
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• 1999

A new control method for precision robust position control of a brushless DC (BLDC) motor using asymptotically stable adaptive load torque observer is presented in the paper. Precision position control is obtained for the BLDC motor system approximately linearized using the field-orientation method Recently, many of these drive systems use BLDC motors to avoid backlashe. However, the disadvantages of the motor are high cost and complex control because of nonlinear characteristics. Also, the load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in [1] using fixed gain. However, the motor flux linkage is not exactly known for a load torque observer. There is the problem of uncertainty to obtain very high precision position control. Therefore a model reference adaptive observer is considered to overcome the problem of unknown parameter and torque disturbance in this paper. The system stability analysis is carried out using Lyapunov stability theorem. As a result, asymptotically stable observe gain can be obtained without affecting the overall system response. The load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent current which gives fast response. The experimenta results are presented in the paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.730-737
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• 2001

In this paper, an stabilization control method using adaptive neuro-fuzzy controller(ANFC) is proposed for modeling of nonlinear complex systems. The proposed adaptive neuro-fuzzy controller implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks from input and output data of processes. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.