• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.1719-1728
• /
• 2017

Recent concerns about environmental conditions have triggered the growing interest in using green energy resources. These sources of energy, however, bring new challenges mainly due to their uncertainty and intermittency. In order to alleviate the concerns on the penetration of intermittent energy resources, this paper investigates impacts of realizing demand-side potentials. Among different demand-side management programs, this paper considers demand response wherein consumers change their consumption pattern in response to changing prices. The research studies demand response potentials from different load sectors on generation system well-being. Consumers' sensitivity to time-varying prices is captured via self and cross elasticity coefficients. In the calculation of well-being indices, sequential Monte Carlo simulation approach is accompanied with fuzzy logic. Finally, IEEE-RTS is used as the test bed to conduct several simulations and the associated results are thoroughly discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.79-82
• /
• 1997

The current from the feed motor of a machine tool contains substantial information about the machining state. There have been many researches that investigated the current as a measure for the cutting forces. However it has not been reported that indirect measurement of the cutting forces from the current of the feed motor at rest is possible. The cutting force normal to the machined surface influences the machined surface of the workpiece, which makes it necessary to estimate this force to control the roughness of the machined surface. But the unpredictable behavior of the current prevents applying the current to prediction of the cutting state. In this paper, empirical approach was conducted to resolve the problem. Also parametric adaptive and fuzzy logic control strategies are applied to the force regulation problem. As a result, the current is shown to be related to the accumulation of the infinitesimal rotation of the motor, and besides the unpredictable behavior of the current is shown to be caused by the relationship. Subsequently the relationship between the current and the cutting force is identified, and it is presented that control of machined surface using the current of the feed motor at rest is possible.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1079-1082
• /
• 1996

To automate an excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition, operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. The objective of this study is to design a fuzzy logic controller(FLC) which controls the position of excavator's attachment. This approach enables the transfer of human heuristics and expert knowledge to the controller. Excavation experiments are carried out to check the performance of the FLC.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.557-567
• /
• 2005

The increasing complexity of the socio-economic environments makes it less and less possible for single decision-maker to consider all relevant aspects of problem. Therefore are, many organizations employ groups in decision making. In this paper, we present a multiperson decision making method using fuzzy logic with linguistic quantifier when each of group members specifies imprecise judgments possibly both on performance evaluations of alternatives with respect to the multiperson criteria and on the criteria. Inexact or vague preferences have appeared in the decision making literatures with a view to relaxing the burdens of preference specifications imposed to the decision-makers and thus taking into account the vagueness of human judgments. Allowing for the types of imprecise judgments in the model, however, makes more difficult a clear selection of alternative(s) that a group wants to make. So, further interactions with the decision-makers may proceed to the extent to compensate for the initial comforts of preference specifications. These interaction may not however guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfying solution by the use of linguistic quantifier guided aggregation which implies fuzzy majority. This is an approach to combine a prescriptive decision method via a mathematical programming and a well-established approximate solution method to aggregate multiple objects.

• Proceedings of the IEEK Conference
• /
• 2000.11c
• /
• pp.25-28
• /
• 2000

In this paper, an effective intention reading scheme is proposed for human-friendly interface. Soft computing techniques such as fuzzy logic and artificial neural networks are used for this. And Gabor filter based feature(GG feature) is also proposed to deal with local activity in the human face. It is based on human visual system and Gabor filter based approach is very popular in these days. The proposed scheme is adopted for human-friendly interface for rehabilitation service robotic system KARES II.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.3 no.2
• /
• pp.36-44
• /
• 1993

Not all business executives can afford the time or cost of having an expert interpret the output of management science models. They find these models perplexing because they are given in the form of numeric vectors or metrics. In this paper, we discuss the possibility of developing an expert system to assist managers' interpretation of the models' results. Having gained interpreting skills, these executive may integrate the system with commercial software.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.2
• /
• pp.169-174
• /
• 2005

A neural networks (NN) saturation compensation scheme for DC motor systems is presented. The scheme that leads to stability, command following and disturbance rejection is rigorously proved. On-line weights tuning law, the overall closed loop performance and the boundness of the NN weights are derived and guaranteed based on Lyapunov approach. The simulation and experimental results show that the proposed scheme effectively compensate for saturation nonlinearity in the presence of system uncertainty.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1996.10a
• /
• pp.532-539
• /
• 1996

Existing assessment methodologies present a considerable problem because of fuzzy situation of deterioration mechanism of concrete bridges; namely, qualitative, subjective or inconsistent. This paper discusses current assessment methods in aspect of uncertainty. The expert system, COBDA, is developed for consistent and fast assessment of deteriorantion of concrete bridges. Briefly introduced in this paper are the structure of expert system and several methodologies for decision making of deterioration situation and providing repair option. COBDA is configured by PROLOG for logic approach and expert system shell based on Bayesian subjective probability. The methodologies are illustrated and discussed by comparison of condition assessment results in a case study.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.4
• /
• pp.499-505
• /
• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.5
• /
• pp.1034-1041
• /
• 2014

This paper propose a fuzzy inference model for obstacle avoidance for a mobile robot with an active camera, which is intelligently searching the goal location in unknown environments using command fusion, based on situational command using an vision sensor. Instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. In this paper, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. We describe experimental results obtained with the proposed method that demonstrate successful navigation using real vision data.