F.H.F. Leung

The Hong Kong Polytechnic University, Hong Kong, Hong Kong

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Publications (150)109.28 Total impact

  • S.H. Ling, P.P. San, K.Y. Chan, F.H.F. Leung, Y. Liu
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    ABSTRACT: In this paper, an intelligent swarm based-wavelet neural network for affective mobile designed is presented. The contribution on this paper is to develop a new intelligent particle swarm optimization (iPSO), where a fuzzy logic system developed based on human knowledge is proposed to determine the inertia weight for the swarm movement of the PSO and the control parameter of a newly introduced cross-mutated operation. The proposed iPSO is used to optimize the parameters of wavelet neural network. An affective design of mobile phones is used to evaluate the effectiveness of the proposed iPSO. It has been found that significantly better results in a statistical sense can be obtained by the iPSO comparing with the existing hybrid PSO methods.
    Neurocomputing 01/2014; 142:30–38. · 1.63 Impact Factor
  • J.C.Y. Lai, F.H.F. Leung, S.H. Ling
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    ABSTRACT: Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a Fuzzy Inference System (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, and change of QTc are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two Wavelet Mutated Differential Evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification.
    Applied Soft Computing. 01/2014;
  • J.C.Y. Lai, F.H.F. Leung, S.H. Ling, H.T. Nguyen
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    ABSTRACT: In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, change of QTc and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification.
    Applied Soft Computing. 05/2013; 13(5):2803–2811.
  • S. H. Ling, P. P. San, H. T. Nguyen, F. H. F. Leung
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    ABSTRACT: Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
    International Journal of Computational Intelligence and Applications 02/2013; 11(04).
  • G.Y. Wong, F.H.F. Leung, Sai-Ho Ling
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    ABSTRACT: Imbalanced datasets are commonly encountered in real-world classification problems. However, many machine learning algorithms are originally designed for well-balanced datasets. Re-sampling has become an important step to preprocess imbalanced dataset. It aims at balancing the datasets by increasing the sample size of the smaller class or decreasing the sample size of the larger class, which are known as over-sampling and under-sampling respectively. In this paper, a novel sampling strategy based on both over-sampling and under-sampling is proposed, in which the new samples of the smaller class are created by the Synthetic Minority Over-sampling Technique (SMOTE). The improvement of the datasets is done by the evolutionary computational method of CHC that works on both the minority class and majority class samples. The result is a hybrid data preprocessing method that combines both over-sampling and under-sampling techniques to re-sample datasets. The evaluation is done by applying the learning algorithm C4.5 to obtain a classification model from the re-sampled datasets. Experimental results reported that the proposed approach can decrease the over-sampling rate about 50% with only around 3% discrepancy on the accuracy.
    Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE; 01/2013
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    ABSTRACT: This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCM method performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
  • G.Y. Wong, F.H.F. Leung, Sai-Ho Ling
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    ABSTRACT: Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. When the scores are calculated from these methods, the method of classification is very important and can affect the prediction results greatly. A developed support vector machine (SVM) is used to classify the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential, offset from protein, conservation score and the information around the pockets. Since SVM is sensitive to the input parameters and the positive samples are more relevant than negative samples, differential evolution (DE) is applied to find out the suitable parameters for SVM. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.
    Neural Networks (IJCNN), The 2012 International Joint Conference on; 01/2012
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    ABSTRACT: In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.
    Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on; 01/2012
  • S. H.ling, F. H. F.leung, L. K.wong, H. K.lam
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    ABSTRACT: The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on achieving the load balancing.
    International Journal of Computational Intelligence and Applications 11/2011; 05(03).
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    H. K.lam, F. H. F.leung
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    ABSTRACT: This paper investigates the synchronization of chaotic systems subject to parameter uncertainties. Based on the fuzzy-model-based approach, a switching controller will be proposed to deal with the synchronization problem. The stability conditions will be derived based on the Lyapunov approach. The tracking performance and parameter design of the proposed switching controller will be formulated as a generalized eigenvalue minimization problem which can be solved numerically using some convex programming techniques. Simulation examples will be given to show the effectiveness of the proposed approach.
    International Journal of Bifurcation and Chaos 11/2011; 16(05). · 0.92 Impact Factor
  • F. H. F.leung, S. H.ling, H. K.lam
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    ABSTRACT: This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA.
    International Journal of Computational Intelligence and Applications 11/2011; 07(04).
  • J.C.Y. Lai, F.H.F. Leung, Sai-Ho Ling, E.C. Shi
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    ABSTRACT: In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the IW till the end of the searching process. A wavelet based mutation process is embedded in the DE searching process to enhance the searching performance. The weight F of DE is also controlled by the fuzzy controller to further speed up the searching process. The proposed method is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration and performs significantly better than the conventional methods in terms of convergence speed and solution quality.
    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on; 07/2011
  • Edwin Chao Shi, F. H. Frank Leung, Johnny C. Y. Lai
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    ABSTRACT: Differential Evolution (DE) is one of the evolutionary algorithms under active research. It has been successfully applied to many real world problems. In this paper, an improved DE with a novel mutation scheme is proposed. The improved DE assigns a distinct scale factor for each individual mutation based on the fitness associated with each base vector involved in the mutation. With the adoption of different scale factors for mutation, DE is capable of searching more locally around superior points and explore more broadly around inferior points. Consequently, a good balance between exploration and exploitation can be achieved. Also, an adaptive base vector selection scheme is introduced to DE. This scheme is capable of estimating the complexity of objective functions based on the population variance. When the problem is simple, it will tend to select good vectors as base vector which will lead to quick convergence. When the objective function is complex, it will select base vector randomly so that the population maintains a high exploration capability and will not be trapped into local minima so easily. A suite of 12 benchmark functions are used to evaluate the performance of the proposed method. The simulation result shows that the proposed method is promising in terms of convergence speed, solution quality and stability.
    Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011; 01/2011
  • J.C.Y. Lai, F.H.F. Leung, S.H. Ling
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    ABSTRACT: An improved differential evolution (DE) that incorporates a wavelet-based mutation operation to control the scaling factor is proposed. The wavelet theory applied is to enhance DE in exploring the solution spaces more effectively for better solutions. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of convergence speed, solution quality and solution stability.
    Evolutionary Computation, 2009. CEC '09. IEEE Congress on; 06/2009
  • Chun Wan Yeung, F. H. Frank Leung, Kit Yan Chan, Sai-Ho Ling
    International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, Georgia, USA, 14-19 June 2009; 01/2009
  • C.W. Yeung, F.H.F. Leung, K.Y. Chan, S.H. Ling
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    ABSTRACT: To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA microarray studies produce a large amount of data, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to the studied classes. Only a fraction of genes may present distinct profiles for different classes of samples. Classification tools to deal with these issues are thus important. These tools should learn to robustly identify a subset of informative genes embedded in a large dataset that is contaminated with high dimensional noises. In this paper, an integrated approach of support vector machine (SVM) and particle swarm optimization (PSO) is proposed for this purpose. The proposed approach can simultaneously optimize the selection of feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied to search the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one out of fourteen patient samples, suggesting that the selected gene signatures can reflect the chemo-resistance in osteosarcoma.
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on; 01/2009
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    S. H. LING, F. H. F. LEUNG, H. K. LAM, H. H. C. IU
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    ABSTRACT: This paper presents a novel neural network with a variable structure, which is trained by a real-coded genetic algorithm (RCGA), and its application on short-term load forecasting. The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches are introduced in the links between the hidden and output layers. By using the NSC to control the on-off states of the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. It gives better results and learning ability than the fixed-structure network with link switches (FSNLS) (3), wavelet neural network (WNN) (25) and feed-forward fully-connected neural network (FFCNN) (9). In this paper, an improved RCGA (2) is used to train the parameters of the VSNN. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.
    International Journal of Information & Systems Sciences. 01/2009; 5(1).
  • C.W. Yeung, S.H. Ling, Y.H. Chan, F.H.F. Leung
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    ABSTRACT: Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Most existing restoration algorithms are generally inadequate to deal with this problem as they were proposed for restoring noisy blurred images. In this paper, a restoration algorithm based on particle swarm optimization with wavelet mutation (WPSO) is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a half-toned color-quantized image remarkably in terms of both SNRI and convergence rate. The subjective quality of the restored images can also be improved.
    TENCON 2008 - 2008 IEEE Region 10 Conference; 12/2008
  • S.H. Ling, H.H.C. Iu, F.H.F. Leung, K.Y. Chan
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    ABSTRACT: An hybrid particle swarm optimization PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid dispensing process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid dispensing process on electronic packaging is employed to demonstrate the effectiveness of the proposed method.
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on; 07/2008
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    H.K. Lam, M. Narimani, J.C.Y. Lai, F.H.F. Leung
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    ABSTRACT: This paper investigates the system stability of T-S fuzzy-model-based control systems based on an improved fuzzy Lyapunov function. Various non-PDC (parallel distribution compensation) fuzzy controllers are proposed to close the feedback loop. The characteristic of T-S fuzzy model is considered to facilitate the stability analysis. Under a particular case, the time-derivative information of the membership functions vanishes, which simplifies the stability analysis and leads to relaxed stability analysis results. A general case is then considered. An improved non-PDC fuzzy controller is proposed based on the properties of the T-S fuzzy model. The improved non-PDC fuzzy controller exhibits a favourable property to relax the stability conditions. Based on the fuzzy Lyapunov function, stability conditions in terms of linear matrix inequalities are derived to guarantee the system stability. Simulation examples are given to illustrate the effectiveness of the proposed non-PDC fuzzy control schemes.
    Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008

Publication Stats

1k Citations
109.28 Total Impact Points

Institutions

  • 1992–2014
    • The Hong Kong Polytechnic University
      • Department of Electronic and Information Engineering
      Hong Kong, Hong Kong
  • 1995–2009
    • The University of Hong Kong
      • Department of Electrical and Electronic Engineering
      Hong Kong, Hong Kong
  • 2008
    • City University of Hong Kong
      • Department of Electronic Engineering
      Kowloon, Hong Kong
  • 2006–2008
    • King's College London
      • • Division of Engineering
      • • Department of Electronic Engineering
      London, ENG, United Kingdom
  • 2007
    • University of Western Australia
      • School of Electrical, Electronic and Computer Engineering
      Perth City, Western Australia, Australia