Yongxuan Huang

Topology Theory on Rough Sets

For further studying the theories and applications of rough sets (RS), this paper proposes a new theory on RS, which mainly includes topological space, topological properties, homeomorphism, and its properties on RS by some new definitions and theorems given. The relationship between partition and countable open covering is discussed, and some applications based on the topological rough space and its topological properties are introduced. Moreover, some perspectives for future research are given. Throughout this paper, the advancements of the new theory on RS and topological algebra not only represent an important theoretical value but also exhibit significant applications of RS and topology.

Research of Enterprise Resource Planning in a Specific Enterprise

Enterprise resource planning (ERP) systems for manufacturing sector are commercially available. However, most existing commercial systems are likely to have some problems, and it can be hard to apply these general ERP systems to the task of resource planning in a specific enterprise. From integrated systems engineering perspectives, this paper presents a research of ERP for an enterprise with unique features. Based on the analysis of necessities, the methods and models of developing an ERP system for the enterprise are discussed and designed.

An industrial information integration approach to in-orbit spacecraft

ABSTRACT To operate an in-orbit spacecraft, the spacecraft status has to be monitored autonomously by collecting and analysing real-time data, and then detecting abnormities and malfunctions of system components. To develop an information system for spacecraft state detection, we investigate the feasibility of using ontology-based artificial intelligence in the system development. We propose a new modelling technique based on the semantic web, agent, scenarios and ontologies model. In modelling, the subjects of astronautics fields are classified, corresponding agents and scenarios are defined, and they are connected by the semantic web to analyse data and detect failures. We introduce the modelling methodologies and the resulted framework of the status detection information system in this paper. We discuss system components as well as their interactions in details. The system has been prototyped and tested to illustrate its feasibility and effectiveness. The proposed modelling technique is generic which can be extended and applied to the system development of other large-scale and complex information systems.

Target recognition by Fast Optimal Fuzzy C-Means image segmentation

We propose a novel Fast Optimal Fuzzy C-Means (FOFCM) clustering algorithm to improve target recognition in image processing. FOFCM can find the best clustering number of images by exploiting the characteristics of the given images, and reduce the segmentation time significantly at the same time. Experiments on serials images are employed to demonstrate the performance of FOFCM. The experiment results show that FOFCM has significantly better performance and lower complexity than previously proposed approaches. The correct recognition rate is increased by 29.24%, which is 94.59%. The clustering efficiency is improved by 6–132 times.

A Bi-level Blocked Estimation of Distribution Algorithm with local search for Maximum Clique Problems

Maximum clique problem (MCP) is a complicated deceptive problem for estimation of distribution algorithms (EDAs). The univariate EDAs cannot utilize the correlations of the variables and the advanced EDAs perform poor due to the expensive computational cost in building the appropriate probability models. In this paper, by utilizing the special structure of MCP, a new Bi-level Blocked Probability model (BBP) is constructed, which achieves the relationships utilizing in a bivariate probability model at the computational cost of univariate probability model. Integrating promising neighborhood search techniques, a new EDA algorithm, called Bi-level Blocked Estimation of Distribution Algorithm (BBEDA) is proposed for MCP. Comparative experiments on extensive DIMACS Benchmark instances show that the proposed BBEDA can be competitive with the evolutionary algorithm with guided mutation (the best evolutionary algorithm reported so far) in terms of solution quality and computational performance.

Calibration of traffic dynamics models with data mining

Speed-density relationships are one of models used by a mesoscopic traffic simulator to represent traffic dynamics. While the classical speed-density relationships provide a useful insight into the traffic dynamics problem and have theoretical value to traffic flow, for such applications they are limited This paper focuses on calibrating parameters for the speed-density relationships by using data mining methods such as locally weighted regression, k -means, k -nearest neighborhood classification and agglomerative hierarchical clustering. Meanwhile, in order to improve the precision of the parametric calibration, we also utilize densities and flows as variables to calibrate parameters. The proposed approach is tested with sensor data from the 3rd ring road in Beijing. The test results show that the proposed algorithm has great performance on the parametric calibration of the speed-density relationships.

Research on methods to simulate spacecraft systems

System simulation plays very important role in spacecraft engineering and is an essential part of spacecraft technology. Three simulation methods used to test and verify performances and functions of spacecraft and five simulation methods to establish a universal simulation system for spacecraft ground TT&C system are proposed. A simulation system for spacecraft, especially for control system of the spacecraft, is analyzed.

Satellite recovery control strategy based on attitude error information fusion

Reentry braking maneuver is the most important TT&C operation of recovery satellites. After finishing appointed missions, a recovery satellite needs to reenter into the atmosphere, land on the ground, and be recovered. During the recovery process, it is necessary for the satellite to perform a series of maneuver or control operations, such as adjusting body attitude, separating recovery capsule from orbit capsule, igniting braking rocket, etc. The ignition attitude information of braking rocket has a close relationship with the length of the reentry trajectory and size of the landing area. So, attitude error information is the key factor to determining the recovery control strategy of the satellite. In order to shorten reentry trajectory length, increase coverage of TT&C, increase recovery control accuracy and narrow landing area size, a new method to make recovery control strategies for the satellites is proposed based on attitude error information fusion.

Application of uncertainty reasoning theory to satellite fault detection and diagnosis

Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some faults of the satellite automatically only by use of certainty reasoning theories. The reason is that detection and diagnosis of these faults require a rational reasoning and a fault-tolerant capability. Fortunately, uncertainty reasoning theories can meet these requirements. It is attracting attention of many experts in the space field all over the world that uncertainty reasoning theories are applied to detect and diagnose satellite faults. Uncertainty reasoning theories include several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. Inclusion degree theory, rough set theory and evidence reasoning theory are three advanced ones. Based on these three theories respectively, this paper introduces three new methods to detect and diagnose satellite faults. It is shown that the methods, suitable for detecting and diagnosing satellite faults, especially uncertainty faults, can remedy the defects of the current methods.

Optimization algorithm of initial orbit based on internal penalty function method

In order to track spacecrafts in boost phase, many kinds of TT&C devices are used, including radar devices, optical devices, unified TT&C devices, satellite tracking devices and rocket tracking devices. Because of the differences between the observation scopes of different TT&C stations, different devices and different TT&C systems, it is necessary to process the data separately by sort instead of process unified, which leads to several different initial orbit elements. How to get one most optical or accurate orbit by calculation will be important to the initial orbit control and flight performance. In this article, first, primary analysis of many sorts of data sources for initial orbit determination is carried out. Second, based on the characteristics of initial orbit for spacecraft, and with system optimization theory, one internal penalty function method for initial orbit optimization is given. The rationality and correctness is validated by practical calculation. The method provides an academic support for improving the precision of initial orbit calculation.