• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1560-1563
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• 1987

This paper is concerned with time delay modeling of ED MOS gates which takes into account the slope of input waveform as well as the load condition. Defining the delay time as the time required to charge/discharge the load to the physical reference level, the rise/fall delay times arc derived in an explicit formula in terms of the sum of optimally weighted current unbalances at two end points of voltage transition. The proposed model is computationally effective and the error is typically within 10% of the SPICE results.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1134-1139
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• 2009

Haul routes for earthmoving operation need to be carefully selected because the decision on the haul routes could make a significant difference in the project's cost and time. This paper proposes a decision support system for improving productivity of earthmoving operation that helps construction managers choose the best haul routes of trucks based on time slots. Also, a methodology for optimal selection of haul routes considering traffic conditions and topographic conditions of the routes is explained. Raster data model is used to find an available shortest path based on cost weighted distance. A system was developed on a geographic information system environment for efficient database management and easy manipulation of graphical data. A real-world case study was conducted to verify the applicability of the proposed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1593-1598
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• 2012

In this paper, we propose an intelligent signal control method based on fuzzy logic applicable in real time. We design membership functions to model occupied time and the number of vehicles for each lane. A priority for each signal phase is computed by the popular Max-Min fuzzy inference based on control rules and membership degrees of prepared two functions at any given time. A tie breaking scheme is considering weighted sum of the rate of occupied time per number of vehicles in that block and the standard deviation of these blocks. Only a signal phase with the highest priority is opened and all others are closed and the duration of the phase opening is computed proportional to the rate of number of weighting vehicles in that signal per all weighted vehicles. The simulation result shows that the proposed method is more efficient than the static control in all simulation conditions in $2{\times}3$ experimental designs with the number of vehicles in intersection and congestion degrees that have all three levels.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1030-1041
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• 1997

The control model in the tandem cold rolling mill consists of many mathematical theories and is used to calculate the reference values such as the roll gap and the rolling speed for good operation of rolling mill. But, the control model used presently has a problem causing inaccurate prediction of the rolling force. By the parameter identification, it was found that the main factor causing inaccurate prediction of the rolling force was incorrect modeling of the friction coefficient and the flow stress. To get rid of the erroneous factor new adaptive schemes are suggested in this work. Those are a long-time adaptation by the iterative least-square method and a short-time adaptation by the recursive weighted least-square method respectively. The new equations for the friction coefficient and the flow stress are derived by applying the suggested adaptive algorithms. Through the on-line test in an actual mill, it is proved that the rolling force predicted by the new equations is more accurate than the one by the existing equations ever used.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.65-75
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• 2010

When the cost-benefit analysis is applied for social discount rate(SDR), the choice of SDR to be used in analysis is critical. One of the important issues when public investment project evaluate what is the SDR theory, so there have studied about SDR and no exact answer it so far. In this study, there are three of SDR theories that be estimated social time preference rate, social investment returns and the weighted average method from 1990s, 2000 to 2003 and 2004 to 2008.. First, social time preference method computes consumer's interest rate and the model of Pearce and Ulph(1999). Second, social investment returns method computes private returns of capital. Third, the weighted average method computes the model of Squire, L., Herman G. van der Tak(1975) and private consumption expense and the private investment expense. SDR is estimated in the rage between 2.4% and 3.9% from 2004 to 2008. It is not appropriate that the interest rate was unstable. But it is consider for social equity from present to future generations. Considering this things, downward need to the value of current SDR 5.5%.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.971-981
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• 2010

The objectives of this study were to measure ambient total gaseous mercury (TGM) concentrations in Seoul, to analyze the characteristics of TGM concentration, and to identify of possible source areas for TGM using back-trajectory based hybrid receptor models like PSCF (Potential Source Contribution Function) and RTWC (Residence Time Weighted Concentration). Ambient TGM concentrations were measured at the roof of Graduate School of Public Health building in Seoul for a period of January to October 2004. Average TGM concentration was $3.43{\pm}1.17\;ng/m^3$. TGM had no notable pattern according to season and meteorological phenomena such as rainfall, Asian dust, relative humidity and so on. Hybrid receptor models incorporating backward trajectories including potential source contribution function (PSCF) and residence time weighted concentration (RTWC) were performed to identify source areas of TGM. Before hybrid receptor models were applied for TGM, we analysed sensitivities of starting height for HYSPLIT model and critical value for PSCF. According to result of sensitivity analysis, trajectories were calculated an arrival height of 1000 m was used at the receptor location and PSCF was applied using average concentration as criterion value for TGM. Using PSCF and RTWC, central and eastern Chinese industrial areas and the west coast of Korea were determined as important source areas. Statistical analysis between TGM and GEIA grided emission bolsters the evidence that these models could be effective tools to identify possible source area and source contribution.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1060-1062
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• 1996

This paper presents a discrete adaptive observer for MIMO system of an IM model in DQ reference model. The IM model in the stationary frame is discretized and it is transformed into the canonical observer form. The unknown parameter is choosen as rotor speed. The adaptive law for parameter adjustment is obtained as a set of recursive equations which are derived by utilizing an exponentially weighted normalized least-square method. The proposed adaptive observer converges rapidly and is also shown to track time-varying plant parameter quickly. Its effectiveness has been demonstrated by computer simulation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.625-630
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• 1986

In this paper, an adaptive observer based upon the exponentially weighted least-square method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. The adaptive observer estimates the padrameter vectors and initial state vector. The control input is determined so that the output of the plant converges to the output of the stable model reference.