Hua-Mao Gu’s research while affiliated with Zhejiang Gongshang University and other places

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Multi-sensor arrays have been applied more and more extensively in various fields, especially in the electrochemical industry. Among them, cross-sensitive multi-sensor arrays (e.g. electronic tongues and electronic noses) are the most popular ones. Taking advantage of the cross-sensitive response of voltammetric electronic tongues, a unique fast approach for variable reduction and pattern recognition is proposed in this paper based on a set of well-designed graphical similarity principles. It translates the measurement data of electronic tongues into a net graph containing rich pattern information, and then compares the similarity between the graphs on the levels of topology, geometric shape, and geometric size respectively and finally gives the corresponding similarity distances between the samples for pattern recognition.

High-dimensional data are always a great challenge for post-processing. This paper proposed a new computational model for dimensionality reduction toward the huge data sampled from the multi-sensor array in voltammetric electronic tongue. It employs a linear superposition model of noise for a set of basis functions to describe the serial current responses of cross-sensitive multi-sensor array. And the coefficients of the basis function set, with a much lower dimensionality, are used as the reduced feature data for a sample. More importantly, the values of those coefficients show high consistency with the responses of electrodes, namely, larger currents measured may result in greater coefficient values, and vice versa.According to the theoretical analysis and practical results of the experiments, besides the value-consistency which is never supported by existing approaches, the proposed model also performs very well in the three key factors for evaluating an approach to dimensionality reduction (i.e. reduction rate, reconstruction error, and complexity): (1) strong ability in dimensionality reduction and feature preservation—the reduction rate reaches as high as circa 280–420, and the average reconstruction error is circa 1–5%; (2) linear time complexity—it is applicable to most huge datasets.

Spatial datum acquisition is one of fundamental components and main datum source of most GIS application systems. As a result, providing a universal framework for datum acquisition to satisfy the needs of those professional GIS applications is significant in GIS applications. The framework presented in this paper changes the traditional ways of datum acquisition, thus improving the efficiency greatly and reducing most potential man-made mistakes. Moreover, this paper also addressed those key algorithms employed in the framework and supplied their implementations.

This paper presents a fuzzy ontology taken as a common understanding and unequivocal sharing of the domain knowledge in computer game community. For better representing knowledge in computer game, we have explored the uncertainty information useful for property computing and incorporated it into ontology. After an investigation, we defined two fuzzy relations: damage effectiveness and armor effectiveness, both playing vital roles in property calculating. To build such a fuzzy ontology, we also introduce a use-case based fuzzy ontology constructing (UFOC) methodology that includes use-case analysis and fuzzification processes, and a way of representing fuzzy information with ontology description language.

Based on the statistical characters of remote sensing image, this paper proposed firstly partition remote sensing image into sub-block images of suitable sizes, and then decomposing these sub-block images with biorthogonal 7/5 wavelet. A novel construction method of wavelet set of coefficients is adopted to embed binary watermarking image into sub-bands except low frequency sub-bands, and realizing a blind detection algorithm. The visual model of biorthogonal 9/7 wavelet is extended to construct a visual weighted analysis method based on the biorthogonal 7/5 wavelet domain quantization noises, thus making the strength of the embedded watermarking could self-adapt to the sub-blocks of the remote sensing image. Experimental results show that the proposed algorithm has great imperceptibility and is robust to the image intensity attack such as geometric transformation, noise adding, filtering, and compression. It performs better than that of the watermarking algorithm proposed by Ho in the robust, and being a practical blind watermarking algorithm.

OWL Web Ontology Language is a W3C recommended Language for representing machine-readable knowledge with more facilities than other knowledge representation languages. In this paper, we present a Use-Case based Fuzzy Ontology Constructing (UFOC) methodology for the building of edutainment ontology, which is encoded with OWL. Besides, a solution of representing fuzzy relation in OWL is also provided. In the edutainment ontology, two fuzzy relations, DE and AE are discovered, both playing vital roles in property calculation among online computer game.