• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.203-209
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• 2010

This paper presents a hierarchical fuzzy motion planner for humanoid robots in 3D uneven environments. First, we define both motion primitives and locomotion primitives of humanoid robots. A high-level planner finds a global path from a global navigation map that is generated based on a combination of 2.5 dimensional maps of the workspace. We use a passage map, an obstacle map and a gradient map of obstacles to distinguish obstacles. A mid-level planner creates subgoals that help the robot efficiently cope with various obstacles using only a small set of locomotion primitives that are useful for stable navigation of the robot. We use a local obstacle map to find the subgoals along the global path. A low-level planner searches for an optimal sequence of locomotion primitives between subgoals by using fuzzy motion planning. We verify our approach on a virtual humanoid robot in a simulated environment. Simulation results show a reduction in planning time and the feasibility of the proposed method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.390-393
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• 1997

Fuzzy logic and neural networks are complemetary technologies in the design of intelligent system. Fuzzy neural network(FNN) as an auto-tuning method is actually known to an excellent method for the adjustment of the fuzzy rule. However, this has a weak point, because the convergence to the optimum depends on the initial condition. In this paper we develop a coding format to determine a FNN model by chromosome in GA and present systematic approach to identify the parameters and structure of FNN. The proposed hybrid tuning method realizes to construct minimal and optimal structure of the fuzzy mode simultaneously and automatically. This paper shows effectiveness of the tuning system by simulations compared with conventional methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.87-93
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• 2008

In this paper, we propose a hierarchical fuzzy based nutrition evaluation system that can analyze the individuals' nutrition status through the inference results generated by each layer. Moreover, a method to minimize the uncertainty of inference in the evaluated nutrition status is discussed. To show the effect of the uncertainty in fuzzy inference, we compared the results of nutrition evaluation with/without the certainty factor of rules on 132 people over the age of 65. From the experimental results, we can see that the evaluation method with the modified certainty factor provides better reliability than that of the general evaluation method without the certainty factor.

• Korean Journal of Construction Engineering and Management
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• v.8 no.3
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• pp.159-167
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• 2007

In this study, the concepts of integrated VE analysis assessment is introduced in order to achieve "Design for Deterioration performance" in design VE phase. For this purpose, a framework for fuzzy reliability based LCC and value analysis model using fuzzy logic based approach for breakwaters Projects is suggested. It is anticipated that the methodology Proposed in this paper can also be utilized in the design and maintenance phase of other facilities where decision making is made for the fuzzy reliability based life cycle cost and value analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.616-619
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• 2003

In this paper, we develop an input device equipped with accelerometers and gyroscopes. The installed sensors measure the inertial measurements i.e., accelerations and angular rates produced by the movement of the system when a user is writing on the plane surface or in the three dimensional space. The gyroscope measurement are integrated once to give the attitude of the system and consequently used to remove the gravity included in the acceleration measurements. The compensated accelerations bin doubly integrated to yield the position of the system. Due to the integration processes involved in recovering the users'motions, the accuracy of the position estimation significantly deteriorates with time. Among various error sources of the system incorrect estimation of attitude causes the largest portion of the positioning error since the gravity is not fully cancelled. In order to solve this problem, we propose a Kalman filler-based attitude estimation algorithm which fuses measurement data from accelerometers and gyroscopes by fuzzy logic approach. In addition, the online calibration of the gyroscope biases are performed in parallel with the attitude estimation to give more accurate attitude estimation. The effectiveness and the feasibility of the presented system is demonstrated through computer simulations and actual experiments.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.269-276
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• 2013

Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.