Abstract. In order to measure the weight of yak more conveniently and effectively, based on BP neural network, this paper provides a portable dynamic weighing System for yak. The wireless transmission mode is adopted between the acquisition module and the instrument in this system and weighing platform is added a handle and a small roller table, which overcome the shortcomings of traditional weighing station that cannot move. In order to further improve the problem of traditional weighing station's low accuracy, using the smoothing mean filter to denoise the original data and according to the output of the shear beam type weighing sensor and the speed of yak, the BP neural model is established, thus the static weight of yak being obtained. By many experiments in matlab, the results show that this system achieves the measurement accuracy of dynamic weighing system and ensures that it can be achieved technically, which has good practical value.

Abstract.PHP is an open source general-purpose computer scripting language and especially suitable for network development, and can be embedded in the use of HTML [1]. In this paper, the author used PHP script language and MySQL database to design and implement the yak growth information system based on B/S architecture. The system is easy for the yak researchers and the yak breeding personnel to operate and manage.

Abstract. In this paper the failure sets and symptom sets of the problem for a 1000MW unit were determined. On the basis of distinguishing the precipitous decline and slow decline of vacuum, the calculation model of the state quantization value of every symptom parameter was established and the fault characteristic vector of the lower vacuum of the condenser was obtained by the simulation test of the unit. Based on BP neural network, the fault diagnosis model of condenser was established, and the low vacuum fault of the unit was diagnosed. The results show that the fault diagnosis of condensers can be used in the actual unit operation according to the fault theory domain feature vector of 1000MW unit.

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In large-scale complex system, The establishment of a fast, accurate fault diagnosis system is more difficult because there exist many uncertain elements between the fault cause and the fault sign .A fault diagnosis system is established based on RBF cloud neural network ,the RBR (rule-based reasoning) and the CBR (case-based reasoning).The fault diagnosis system not only has the advantages of self-learning, high accuracy, randomness, fuzziness, etc ,and has the advantages of independently of mathematical model ,rich knowledge representation, mighty problem solving ability, etc. Theoretical analysis and simulation results show that the system is feasible and effective for fast and accurate fault positioning of complex systems.