Key Engineering Materials Vols. 439-440

Abstract: This paper presents a generating algorithm at pixel level for parametric curve. The parallel particle sub-swarm optimization is used to search the optimal step of curve in forward. Large amount of repeated computing for points are avoided and the result is precise enough. Simulation results show that the parallel method based on particle sub-swarm can be used for searching the optimal step of parametric curve with any degree quickly. At the same time, compared with other methods, this algorithm produces the maximum step efficiently. Since there is no restriction on control point position and curve degree, the algorithm can be extended easily to other parametric curves besides Bézier curves.

Abstract: In this paper we propose a general framework for computing similarity between concepts. This framework generalizes the Tversky’s model of similarity and uses a non-negative matrix factorization procedure to estimate abstract classes on which similarity between concepts may be computed. We applied the framework to semantic features used to describe concepts. Experimental results suggest that the general framework is feasible and this method is applicable across different concepts. This framework may be considered as a valuable measurement method to test hypotheses about category-specific disorders.

Abstract: This paper puts forward a designing method of Dependable Network System Security Risk Diagnosis (DSSRD). It gets reduced information table, which implies that the number of evaluation criteria is reduced with no information loss, and then, this table is used to develop classification rules and infer appropriate parameters. It’s capable of overcoming several shortcomings in existing diagnosis methods, such as a dilemma between stability and redundancy. The average speed of training in DSSRD is almost twice fast as that in SMO. The experiment implemented by this method shows a good diagnostic ability.

Abstract: Expanded State Observer (ESO) can be used in the control of plants with uncertainties to achieve better effects. Many theoretical and practical researches have been done on design of systems equipped with ESO. Nevertheless, those on the stability of ESO are relatively rare. In this paper, stability analysis for ESO was conducted based on the methodology of description function. Stability conditions for nonlinear ESO with (2+1)th order were presented. It was proved in this paper that stability conditions that were same with that of linear state observers could be obtained and better tracking performance could be achieved if nonlinear parameters were chosen appropriately. The proposed analysis methodology was also applicable for nonlinear ESO with (n+1)th order.

Abstract: At present, objects dictionary (OD) and object descriptions used in fieldbus intelligent instruments are based on variable-sized storage and constant property values, which constrains the flexibility and portability in data access and modularization and encapsulation in system design. In this paper, a kind of universal data structure for object description in OD was presented. Universal management of object description with different types could be realized with the proposed structure. Based on principle and methodology of Object-Oriented Design (OOD), an improved model for object description was proposed. In the improved model, constant value used in object code field of object description is replaced by function pointer pointing to object instance handler for data process of object instances belonging to the same class. Universal function structure is used for all class object instance handlers for different classes but instances belonging to different classes are manipulated differently with their own handlers. The improved model for OD and object description was proven effective and efficient by the practice of development and design of intelligent instruments in distributed control network.

Abstract: A hybrid genetic algorithm is proposed based on chaos optimization. The optimization process can be divided into two stages every iteration, one is genetic coarse searching and the other is chaos elaborate searching. Genetic algorithm searches the global solutions in the origin space. An elaborate space near the center of superior individuals is divided from the origin space, which is searched by chaos optimization adequately to generate new better superior individuals for genetic operation. The elaborate space can be compressed quickly to accelerate searching rate and enhance the searching efficiency. In this way, the algorithm has global searching ability and fast convergence rate. The simulation results prove that the algorithm can give satisfied results to function optimization problems.

Abstract: Aiming at the features of the common Supply Chain quality management, an intelligent Supply Chain quality management system based on Six Sigma theory was proposed. The Six Sigma workflow, organizational framework and system goal were adopted in the system overall design, and some traditional Six Sigma tools were integrated into the system. A sub-system for expansive intelligent Six Sigma expert model was designed, which solved the problem of knowledge acquisition in the general expert system and endowed the system with the characteristic of local intelligence.

Abstract: Digital Watermarking as the offset of information wrap technology was embedded secret information in digital products in order to protect the copyright. LSB takes the watermark image embedded into the most unimportant places of vector images. This algorithm is very simple, strong real-time, embedded stack information and can be accurate resume embedded information. We have completed needs analysis and delineation of functional modules of image watermarking software. It is easy to use and had realized the basic accession.

Abstract: Rolling bearings are vital elements in rotating machinery and vibration signal is a kind of effective mean to characterize the status of rolling bearing fault. This paper presents a novel intelligent method for fault diagnosis based on empirical mode decomposition, fractal feature parameter extracting and orthogonal quadratic discriminant function classifier. The new method consists of three steps. Firstly, with investigating the feature of impact fault in vibration signals, the raw vibration signals are decomposed into intrinsic mode functions by empirical mode decomposition. Secondly, using the method of time sequences fractal dimension calculating, fractal feature parameters are extracted from intrinsic mode functions. Then, each raw signal sample has a feature set. Finally, training set and testing set are inputted into the orthogonal quadratic discriminant function model in the classification phase to identify different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results indicate that the novel intelligent diagnosis method is sensitive to fault severity and capable of fault detection and fault diagnosis.

Abstract: When there is occlusion, the measurement matrix collecting trajectories of features points in SFM would be incomplete. In this paper, we have presented a method to recover missing elements in an incomplete measurement matrix one by one. We also discussed the concept of Relevance between a target missing element and its relevant known elements, and a measurement matrix transformation process, utilizing in determining the order of the recovery of missing elements. Experiments on both synthetic and real data showed that our method work efficiently.