• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.100-107
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• 2002

The advanced bilateral control algorithm, which can enlarge a reflected force by combining force reflection and compliance control, greatly enhances workability in teleoperation. In this scheme the maximum boundaries of a compliance controller and a force reflection gain guaranteeing stability and good task performance greatly depend upon characteristics of a slave arm, a master arm, and an environment. These characteristics, however, are generally unknown in teleoperation. It is, therefore, very difficult to determine such maximum boundary of the gain. The paper presented a novel method for design of an advanced bilateral controller. The factors affecting task performance and stability in the advanced bilateral controller were analyzed and a design guideline was presented. The neurofuzzy compliance model (NFCM)-based bilateral control proposed herein is an algorithm designed to automatically determine the suitable compliance for a given task or environment. The NFCM, composed of a fuzzy logic controller (FLC) and a rule-learning mechanism, is used as a compliance controller. The FLC generates compliant motions according to contact forces. The rule-learning mechanism, which is based upon the reinforcement learning algorithm, trains the rule-base of the FLC until the given task is done successfully. Since the scheme allows the use of large force reflection gain, it can assure good task performance. Moreover, the scheme does not require any priori knowledge on a slave arm dynamics, a slave arm controller and an environment, and thus, it can be easily applied to the control of any telerobot systems. Through a series of experiments effectiveness of the proposed algorithm has been verified.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.6
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• pp.58-68
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• 2000

In this paper, a mathematical model of describing oxygen density in multi-band type reheating furnaces was presented. Model designed in this paper was composed of majorly two parts. One is a model regarding 'variation of existing gas'. The other is a model of showing 'variation of oxygen content'. Each model is designed by considering four factors related to variation of oxygen density based on chemical reaction, fluid dynamics and fuzzy theory. Four factors to be considered are combustion reaction in burner, fluid transfer between adjacent combustion bands, fluid transfer from furnace's inner space to external space, and input of external air via gates. According to simulation results, it was shown that varying pattern of oxygen density in each combustion band is similar to generally expected operation data in reheating furnace.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.231-235
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• 2001

Insulation aging diagnosis system provides early warning regarding electrical equipment defect. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear, it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a magnetic wave and acoustic signal to diagnoses an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System) and acquires 2D Patterns from analyzing it. For fettering the noise contained in sensor signals we used ICA algorithms. Using this data design of the neuro-fuzzy model that diagnoses an electrical equipment is investigated. Validity of the new method is asserted by numerical simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1074-1081
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• 2010

Today, the research related to the robot is achieved in various part. With the high interest in means of transport, various researches about autonomous mobile robot and next generation transport is continuing. The unicycle robot among these needs much control technique like balance control model and driving model. For autonomous driving of this unicycle robot, from the basic balance control to direction switching control and velocity control are needed. But the environment elements like a gradient and frictional force or unbalanced elements from the structural feature. The unicycle needs the real time balance control so more complex, harder to control. And when functional addition is made, the problem that fall entire reaction velocity or accuracy would be happen. This paper introduces entire dynamics modeling of the unicycle robot and reduced model. And propose the new balance control algorithm using fuzzy controller. Also the evaluation about performance would be made through the test.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1678-1683
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• 2001

The mobility of tracked vehicles is mainly influenced by the interaction between tracks and soil, so that the characteristics of their interactions are quite important fur the tracked vehicle study. In particular, the track tension is closely related to the maneuverability of tracked vehicles and the durability of tracks and suspension systems. In order to minimize the excessive load on the tracks and to prevent the peal-off of tracks from the road-wheels, the Dynamic Track Tensioning System (DTTS) which maintains the optimum track tension throughout the maneuver is required. It consists of track tension monitoring system, track tension controller and hydraulic system. In this paper, a dynamic track tensioning system is developed for tracked vehicles which are subject to various maneuvering tasks. The track tension is estimated based on the idler assembly model. Using the monitored track tension and con sidering the highly nonlinear hydraulic units, fuzzy logic controllers are designed in order to control the track tension. The track tensioning performance of the proposed DTTS is verified through the simulation of the Multi -body Dynamics tool.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.263-269
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• 1998

The forecasting of container cargo volumes should be estimated correctly because it has a key roles on the establishment of port development planning, and the decision of port operating system. Container cargo volumes have a dynamic characteristics which was changed by effect of competitive ports. Accordingly forecasting was needed overall approach about competitive port's development, alternation and information. But, until now, traffic forecasting was not executed according to competitive situation, and that was accomplished at the point of unit port. Generally, considering the competition situation, simulation method was desirable at forecasting because system's scale was increased, and the influence power was intensified. In this paper, considering this situation, the objectives can be outlined as follows. 1) Structural model constructs by System dynamics method. 2) Structural simulation model develops according to modelling of competitive situation by expended SD method which included HEP(Hierarchical Fuzzy Process) And actually, effectiveness was verified according to proposed model to major port in northeast asia.

• Steel and Composite Structures
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• v.52 no.5
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• pp.571-581
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• 2024

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. This work studies the tracking control problem of a class of strict-feedback nonlinear systems with input saturation nonlinearity. Under the framework of dynamic surface control design, RBF neural networks are introduced to approximate the unknown nonlinear dynamics. In order to address the impact of input saturation nonlinearity in the system, an auxiliary control system is constructed, and by introducing a class of first-order low-pass filters, the problems of large computation and computational explosion caused by repeated differentiation are effectively solved. In response to unknown parameters, corresponding adaptive updating control laws are designed. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Distribution Science
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• v.22 no.9
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• pp.13-25
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• 2024

Purpose: This study explores the complex interplay between service quality, customer engagement, and loyalty in the e-commerce sector, examining the moderating effect of technological adoption on these crucial relationships. Research design, data and methodology: Employing a robust multi-method approach, the research analyzes data from 481 e-commerce users, leveraging the complementary strengths of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis(fsQCA). Acomprehensive multi-group analysisis conducted to uncover differences between experienced and non-experienced users. Results: PLS-SEM reveals that service quality significantly influences customer engagement, which in turn drives loyalty. Technological adoption positively moderates the service quality-engagement relationship. The multi-group analysis uncovers notable differences between user segments. fsQCA identifies two distinct configurational paths consistently leading to high customer loyalty: high customer engagement and high service quality. Conclusions: This study's innovative integration of PLS-SEM and fsQCA contributes to a deeper understanding of the intricate dynamics driving e-commerce success. Findings provide actionable insights for e-commerce businesses to enhance service quality, foster engagement, and cultivate loyalty. This research lays the groundwork for further exploration of these critical relationships in different contexts, offering a nuanced perspective on the complex interplay of factors shaping customer behavior in the digital marketplace.