Novel Deng’s Grey Development Relation Model Based on Information Difference and Its Application in Sanatorium Performance Evaluation

In view of the varying amounts of information and the difference in information value in sanatorium performance evaluation, Deng’s development relation model based on information difference is proposed herein. Firstly, the definition of panel data on sanatorium performance evaluation and the construction criteria of its optimal vector are given. Simultaneously, the indicators are standardized. In order to objectively highlight the difference of effective information among indicators, the entropy weight method is introduced to determine the weight of performance evaluation indicators in sanatoriums on the basis of traditional Deng’s correlation model avoiding the limitation of subjective effects. Furthermore, the weight of the temporal index is employed to determine the weight of performance evaluation results in different years, based on the new information priority, to dynamically evaluate the development trend of annual sanatorium performance. Finally, the performances of TS, YZ, and XZ sanatoriums are dynamically evaluated by using the proposed new grey relational model. Combined with the weight of time index, the 5-year comprehensive performance value is given. It is found that the performance level of TS sanatorium is the highest followed by YZ sanatorium and XZ sanatorium, which verifies the practicability and effectiveness of the model.

Optimal Two-Part Tariff Licensing in a Differentiated Mixed Duopoly

This paper considers the two-part tariff licensing by an innovating firm to its potential competitor in a differentiated mixed duopoly, in which one firm sets a quantity and the other firm charges a price. Based on the development cost incurred by the rival, we derive the optimal behavior of the firms under full information case and partial information case respectively. Information difference on the equilibrium strategies is also investigated.

Shape and Boundary Similarity Features for Accurate HCC Image Recognition

Nucleus morphology is of great importance in conventional cancer pathological diagnosis, which could provide information difference between normal and abnormal nuclei visually. Therefore, this paper proposes two novel kinds of features for normal and hepatocellular carcinoma (HCC) nucleus recognition, including shape and boundary similarity. First, each individual nucleus patch with the fixed size is obtained using center-proliferation segmentation (CPS) method. Then, nucleus shape library is constructed based on manual selection by pathologists, which is utilized to measure nucleus shape similarity via Dice, Jaccard, precision, and recall coefficients. Meanwhile, boundary similarity is evaluated through triangles composed of some boundary feature points for each nucleus. Finally, the conventional random forest (RF) is used to train and test the classification model for HCC nucleus recognition. Extensive cross-validation tests could facilitate the selection of the optimal feature set and the experiment comparison results demonstrate that our proposed morphological features are more beneficial for classification compared with other traditional characteristics.

Fabric Defect Detection Based on Cross-Entropy

The core of fabric defects detection is the collection and processing of fabrics image. A scheme for fabric defect detection based on cross-entropy is proposed in this paper.The crossentropy value illuminates the information difference between the template image and the realtime image on the average.So can take advantage of cross-entropy criteria to use for defect detection and identification. Results have confirmed the usefulness of this scheme for fabric defect detection.

Gaussian-Geometric Moments and its Application in Feature Matching

Since the 7 famous Hus invariants had been introduced in 1960s, the moment invariants play an important role in image analysis and pattern recognition. In this paper, we propose a new moment called Gaussian-Geometric moment, and derived their translation and rotation invariants. One significant conclusion drawn is that the rotation invariants of Gaussian-Geometric moments have the identical forms to those of geometric moments.The Gaussian-Geometric moments and the geometric moments both can represent the image information, difference is that the Gaussian-Geometric moment can represent the center information of an image and the geometric moments represent the edge information of an imageonly. This is particularly evident in the performance of high order ones. Another important property of Gaussian-Geometric moments is that it has a scale parameter which allows choosing the best scale to represent the interest region of an image. A detailed comparison has been made to test the feature matching capability between the proposed moments and the geometric moments. The results show that the proposed moments perform much better than the geometric ones.

Study on Parameters Configuration for Ant Colony Optimization

To find the parameters’ configuration relationship of the Ant Colony Algorithm, based on the ecological actions about ants, the distributing multiformity of ant colony pheromone, the pheromone updating strategy and the mutant of information difference were applied to Microhabitat Ant Colony Optimization (MACO). The parameters, α0, β0, kα and kβ of MACO were configured by the orthogonal experiment to enhance the performance of the algorithm, in which the interactions of α0 and β0, kα and kβ, α0 and kα, β0 and kβwere also analyzed. Some benchmarks of TSP and JSSP were solved by MACO which showed significant optimize performance with configured parameters.