Applied Mechanics and Materials Vols. 602-605

Abstract: In the firing process of ceramic products, the sintering conditions vary from firing phase to firing phase. In different firing phases, flame texture changes obviously, so it can be used as a important parameter of burning zone identification for ceramic roller kiln. In this paper, both flame image recognition of simulating artificial-look-fire and multi-point temperature detection technology are used to detect burning zone working conditions of ceramic roller kiln so as to greatly improve detection accuracy. The key data fusion algorithm of PTCR-based point detection temperature and flame image recognition–based detection method of burning zone working condition for ceramic roller kiln are proposed. The temperature measurement experiment system scheme of ceramic roller kiln burning zone is also given. The system can fuse the key process data with flame image characteristics so as to get the comprehensive database used to judge burning zone working conditions and temperatures. In the end, The testing experiment was carried out. The experimental results show that the method proposed above is feasible and effective.

Abstract: Passive time difference detection method is distance, high speed and good concealment which has broad military application prospects. One of the key technologies for passive detection is to extract the time lag through effective signal processing. Relevant method is the most basic method to estimate the time difference and is the basic theory of all correlative time-delay estimation algorithms. The method is simple. But good results rely on the spectrum characteristics of signal and noise is ideal. Time delay estimation based on Hilbert transform is the expansion of the generalized correlation time-delay estimation method which changes the correlation function from accidentally symmetry into odd symmetry. Detecting correlation peak is converted into zero crossing detection. The method sharps the main peak value point and improved the precision of time delay estimation which gets better time-delay estimation performance in the narrowband signal.

Abstract: A lot of prior information in complex system test has been accumulated. To use the prior information for complex system testability quantitative analysis, a new complex system testability modeling and analyze method based on Bayesian network is presented. First, the complex system’s testability model is built using various kind of prior information by Bayesian network learning algorithm. Then, the way of assessing the testability of complex system is provided using the inference algorithm of Bayesian network. Finally, some proper examples are provided to prove the method’s validity.

Abstract: Focusing on the disturbance of moving cast shadow, a Bagging-ensemble-based moving cast shadow removal method is proposed. Collecting shadow discrimination features from multiple shadow discrimination models, a shadow detector is trained by employing Bagging ensemble based learning framework. The shadow detector can automatically select effective shadow discrimination features and be updated online adaptively. Experimental results demonstrate the effectiveness of the proposed method.

Abstract: To solve the problem that yield monitoring cannot be done in real time during tomato harvesting in existing technology, a real-time yield monitor device on tomato harvester is designed by an three-dimensional software which is named Solidworks. This article describes the all structural characteristics and working principle of the device. Weighing belt, rollers and three kinds of sensors of the device are mainly designed and selected based on the working environment and technical requirements. The design of yield monitor device promotes popularization of the technology of tomato harvesting yield monitor on tomato harvesting machinery, makes the process of yield monitor in real time during tomato harvest realize automated, intelligently and informationized and provides a theoretical basis for the further study of tomato harvester monitoring technology.

Abstract: With the development of information technology, especially the rapid development of network transmission technology, remote monitoring is becoming more and more popular, remote monitoring system (RMS) for CNC machines will be a trend in the control field. This paper introduces research and application status of RMS for CNC machines interiorly and abroad, summarizes its main features,discusses specific technical routes. Finally,problems and suggestions in the development are presented.

Abstract: In view of the grass-roots level radar equipment maintenance and testing difficulty is big, the efficiency is low, limited technical conditions, etc, put forward a kind of intelligent fault diagnosis expert system model suitable for the radar equipment, and focus on the basic structure of the model, knowledge acquisition and the relevant reasoning mechanism. According to the characteristics of the grass-roots level radar fault diagnosis, the system combines automatic test technology and expert system and can improve the efficiency and reliability of fault diagnosis.

Abstract: The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development which is consistent, extensible and easily manageable, with low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various types of attack. DoS or Probing attack are relatively more common, and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.

Abstract: In order to diagnose nonlinear and non-stationary fault signals in bearings, a new method is presented based on the ensemble empirical decomposition (EEMD) and the fuzzy c-means (FCM) clustering algorithm. At first, the bearing fault signals were decomposed using EEMD and the intrinsic mode functions (IMF) were produced. Second the energy ratios of these IMFs were computed and taken as the characteristic parameters for the FCM clustering algorithm. Then the FCM clustering method was conducted to classify the bearing fault signals into different classes. Finally, on the basis of the preceding classification results, we diagnosed a bearing fault through taking its distances between different cluster centers as the criteria. Experiments showed that the bearing fault signal classification results conformed to actualities well. The new signal classification method can be effectively utilized in bearing fault diagnosis.

Abstract: Forced power oscillation in power systems has a serious influence to the safe and stable operation of power systems. A fast dynamic sample entropy algorithm is proposed based on sample entropy in this paper. The change of dynamic sample entropy of the tie-line power is analyzed to determine whether the forced power oscillation happened. Case study on the 4-machine 2-area system shows the effectiveness and efficiency of the proposed methodology.