Ming-Hu Ha

Bio: Ming-Hu Ha is an academic researcher from Hebei University. The author has contributed to research in topics: Fuzzy logic & Fuzzy classification. The author has an hindex of 9, co-authored 71 publications receiving 351 citations. Previous affiliations of Ming-Hu Ha include Hebei University of Engineering.

Applications of wavelet transform in medical image processing

Abstract: The wavelet transform and inverse transform algorithm are introduced. The medical image plays an important role in clinical diagnosis and therapy of doctor and teaching and researching. This paper gives reviews of some applications in medical image with wavelet, such as ECG signal processing, EEG signal processing, medical image compression, medical image reinforcing and edge detection, medical image register. With the further development of wavelet theory, wavelet transform be widely applied to the domain of medical image.

Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure

Abstract: In the study of weighted fuzzy production rules (WFPRs) reasoning, we often need to consider those rules whose consequences are represented by two or more propositions connected by “AND” or “OR”. To enhance the representation capability of those rules, this paper proposes two types of knowledge representation parameters, namely, the input weight and the output weight, for a rule. A Generalized Fuzzy Petri Net (GFPN) is also presented for WFPR reasoning. Furthermore, this paper gives a similarity measure to improve the evaluation method of WFPRs and the multilevel fuzzy reasoning in which the consequences and their certainty factors are deduced synchronously by using a GFPN.

The key theorem and the bounds on the rate of uniform convergence of learning theory on Sugeno measure space

Abstract: Some properties of Sugeno measure are further discussed, which is a kind of typical nonadditive measure. The definitions and properties of g λ random variable and its distribution function, expected value, and variance are then presented. Markov inequality, Chebyshev’s inequality and the Khinchine’s Law of Large Numbers on Sugeno measure space are also proven. Furthermore, the concepts of empirical risk functional, expected risk functional and the strict consistency of ERM principle on Sugeno measure space are proposed. According to these properties and concepts, the key theorem of learning theory, the bounds on the rate of convergence of learning process and the relations between these bounds and capacity of the set of functions on Sugeno measure space are given.

Optical font recognition based on Gabor filter

Abstract: The font recognition of Chinese characters is an important part in OCR (optical character recognition) system. It is also a main technical challenge due to the similarity of different fonts. The reconstruction quality of layout depends on the accuracy of font recognition. However, the prevalent method of font recognition is predominant font recognition based on the fact that the most layouts are printed in a single font, which makes it impossible to reconstruct the original layout. In this paper, an improved font recognition method of individual character is proposed. The approach consists of three steps. In the first step, the guidance fonts are acquired based on Gabor filter optimized with genetic algorithm (GA). Then a single font recognizer is applied to get the matching results with the help of the guidance fonts and the layout knowledge of font typesetting. Finally, the post-processing of font recognition is fulfilled according to the layout knowledge. Experiments were carried out with samples from newspaper and magazines and the results show that the method is of immense practical and theoretical value.

An On-line Multi-CBR Agent Dispatching Algorithm

Abstract: Case-based reasoning (CBR) is an effective and fast problem-solving methodology, which solves new problems by remembering and adaptation of past cases. With the increasing requests for useful references for all kinds of problems and from different locations, keeping a single CBR system seems to be outdated and not practical. Multi-CBR agents located in different places are of great support to fast meet these requests. In this paper, the architecture of a multi-CBR agent system is proposed, where the CBR agents locate at different places, and are assumed to have the same ability to deal with new problem independently. When the requests in a request queue from different places are coming one by one, we propose a new policy of dispatching which agent to satisfy the request queue. Throughout the paper, we assume that the system must solve the coming request by considering only past requests. In this context, the performance of traditional greedy algorithms is not satisfactory. We apply a new but simple approach --- competitive algorithm for on-line problem (called On-line multi-CBR agent dispatching algorithm) to determine the dispatching policy to keep comparative low cost. The corresponding on-line dispatching algorithm is proposed and the competitive ratio is given. Based on the competitive algorithm, the dispatching of multi-CBR agents is optimized.

EEG feature extraction for classifying emotions using FCM and FKM

Abstract: The Electroencephalogram (EEG) is one of the useful biosignals detect the human emotions. This paper discusses on a research conducted to determine the changes in the electrical activity of the human brain related to distinct emotions. We designed a competent acquisition protocol for acquiring the EEG signals under audio-visual induction environment. The EEG data has been collected from 6 healthy subjects with in an age group of 21-27 using 63 biosensors. From the subjective analysis on each emotion, three emotions have been identified with higher agreement. After preprocessing the signals, discrete wavelet transform is employed to extract the EEG parameters. The feature vectors derived from the above feature extraction method on 63 biosensors form an input matrix for emotion classification. In this work, we have used Fuzzy C-Means (FCM) and Fuzzy k-Means (FKM) clustering methods for classifying the emotions. We have also analyzed the performance of FCM and FKM on reduced number of 24 biosensors model. Finally, we compared the performance of clustering the discrete emotions using FCM and FKM on both 64 biosensors and 24 biosensors. Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.

Entropy-based fuzzy support vector machine for imbalanced datasets

Abstract: Imbalanced problem occurs when the size of the positive class is much smaller than that of the negative one. Positive class usually refers to the main interest of the classification task. Although conventional Support Vector Machine (SVM) results in relatively robust classification performance on imbalanced datasets, it treats all samples with the same importance leading to the decision surface biasing toward the negative class. To overcome this inherent drawback, Fuzzy SVM (FSVM) is proposed by applying fuzzy membership to training samples such that different samples provide different contributions to the classifier. However, how to evaluate an appropriate fuzzy membership is the main issue to FSVM. In this paper, we propose a novel fuzzy membership evaluation which determines the fuzzy membership based on the class certainty of samples. That is, the samples with higher class certainty are assigned to larger fuzzy memberships. As the entropy is utilized to measure the class certainty, the fuzzy membership evaluation is named as entropy-based fuzzy membership evaluation. Therefore, the Entropy-based FSVM (EFSVM) is proposed by using the entropy-based fuzzy membership. EFSVM can pay more attention to the samples with higher class certainty, i.e. enhancing the importance of samples with high class certainty. Meanwhile, EFSVM guarantees the importance of the positive class by assigning positive samples to relatively large fuzzy memberships. The contributions of this work are: (1) proposing a novel entropy-based fuzzy membership evaluation method which enhances the importance of certainty samples, (2) guaranteeing the importance of the positive samples to result in a more flexible decision surface. Experiments on imbalanced datasets validate that EFSV outperforms the compared algorithms.