Meng Joo Er

Online speed profile generation for industrial machine tool based on neuro-fuzzy approach

The paper presents the online smooth speed profile generator used in trajectory interpolation in milling machines. Smooth kinematics profile is obtained by imposing limit on the jerk - which is the first derivative of acceleration. This generator is based on the neurofuzzy look-ahead function and is able to adapt online the actual feedrate to changing external conditions. Such an approach improves the machining quality, reduces the tools wear and shortens total machining time.

New Method for Dynamic Signature Verification Using Hybrid Partitioning

Dynamic signature is behavioural biometric attribute which is commonly used to identity verification. Methods based on the partitioning are one of the types of methods for identity verification using signature biometric attribute. These methods divide trajectories of the signature into parts and during verification phase compare created fragments of trajectories in each partition. Partitioning is performed on the basis of values of signals describing dynamics of signing process (e.g. pen velocity or pen pressure). In this paper we propose a new method for dynamic signature verification using hybrid partitioning. Partitions in the proposed method can be interpreted as, for example, high velocity in the first phase of the signing process or low pressure in the final phase of the signing process. Our method assumes use of all partitions during classification process and our classifier is based on the flexible neuro-fuzzy system of the Mamdani type. Simulations were performed using public SVC2004 dynamic signature database.

On the application of the parzen-type kernel regression neural network and order statistics for learning in a non-stationary environment

A problem of learning in non-stationary environment is solved by making use of order statistics in combination with the Parzen kernel-type regression neural network. Probabilistic properties of the algorithm are investigated and weak convergence is established. Experimental results are presented.

A New Method for the Dynamic Signature Verification Based on the Stable Partitions of the Signature

Dynamic signature is a very interesting biometric attribute which is commonly socially acceptable. In this paper we propose a new method for the dynamic signature verification using stable partitions of the signature. This method assumes selection of two the most stable hybrid partitions individually for the signer. Hybrid partitions are formed by a combination of vertical and horizontal sections of the signature. The selected partitions are used during identity verification process. In the test of the proposed method we used BioSecure DS2 database, distributed by the BioSecure Association.

The Idea for the Integration of Neuro-Fuzzy Hardware Emulators with Real-Time Network

In modern industry a real-time Ethernet-based control systems are typically used instead of a centralized solution. This is due to the economical reason and to allow easy expansion and modernization of the machines. The distributed architecture enables i.a. the use of hardware emulators instead of the real control object, in a manner transparent to the whole system. This is an advantage because it can be useful for the development of the control system. It will make that cheap and safe testing of complex control systems will be available. The testing process might be performed in a working control system in which part of it (e.g. a control object) has been temporarily replaced by an emulator. However, emulators typically need an increased performance of the real-time communication interface to transfer a large amounts of the service data. In this paper we propose a new method to create high performance real-time, Ethernet-based communication solution which will be suitable for the most demanding applications, for example for the development process and connection with hardware emulators.

Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach

This paper presents a diagnostic tool to be used to assist building automation systems for sensor heath monitoring and fault diagnosis of an Air-Handling Unit (AHU). The tool employs fault detection and diagnosis (FDD) strategy based on an Efficient Adaptive Fuzzy Neural Network (EAFNN) method. EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent to the Ellipsoidal Basis Function (EBF) neural network neurons. An EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. Simulation works show the proposed diagnosis algorithm is very efficient which can not only reduce the complexity of the network but also accelerate the learning speed.

A New Approach to Designing of Intelligent Emulators Working in a Distributed Environment

The paper proposes a new class of the hardware emulators, namely the remote emulators. They can temporarily replace a control object to allow testing of a distributed system in a safe manner. This method is named a remote-hardware-in-the-loop (RHIL). The second issue described in the paper is a hybrid method of using the computational intelligence in the hardware emulators. This hybrid system is based on a radial-basis-function, a fuzzy-logic and a state variables theory. The proposed solutions make it possible to build a hardware emulator that can work in the RHIL systems with a good accuracy.

The Method of Hardware Implementation of Fuzzy Systems on FPGA

In this paper a method of implementation of fuzzy system on FPGA devices is presented. The method applies to a class of fuzzy systems which are functionally equivalent to a radial basis function networks. In the paper the example fuzzy system was implemented on the FPGA device with the use of the proposed method. The results confirm a high performance of the obtained fuzzy system. This was achieved at a reasonable consumption of the hardware resources of the FPGA.

Stability Evaluation of the Dynamic Signature Partitions Over Time

Analysis of biometric attributes’ changes is an important issue of behavioral biometrics. It seems to be very important in the case of identity verification. In this paper the analysis of features describing the dynamic signature was performed. The dynamic signature is represented by a set of nonlinear waveforms describing dynamics of signing process. The proposed analysis is based on a set of coefficients defined in the context of the dynamic signature partitioning. The partitioning is performed in order to facilitate analysis of the signature. It consists in division of the signature into parts which can be related to e.g. high and low velocity of pen in the initial and final phase of signing. The proposed method was tested using ATVS-SLT DB dynamic signature database.

An efficient adaptive fuzzy neural network (EAFNN) approach for short term load forecasting

In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. It can not only reduce the complexity of the network but also accelerate the learning speed. The proposed EAFNN method is tested on the actual electrical load data from well-known EUNITE competition data. Results show the proposed approach provides the superior forecasting accuracy when applying in the real data.