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The national Lightning Detection Network of the China Meteorological Administration (CMA LDN) has been under construction since 2008. It works in the VLF/LF frequency band to detect Cloud-to-Ground (CG) flashes. It uses the time of arrival and direction finding integrated method to locate, and its operations are stable. The evaluation of the detection efficiency within the network using historical data is close to 70%, while the location accuracy using four stations and above is less than 200 meters. The peak current error is less than 30%. Through the analysis of the lightning monitoring data for 5 years from 2013 to 2017, it is found that the lightning activity as a whole has a downward trend in recent years. There is a large difference in lightning activity in different regions. Generally, the south is the most active, and the flash density is the highest in southern China. The most, especially the average proportion in August is more than 30%.

This paper analyses connections between the input and output in different economic departments. Based on the input-output theory, the relationship among consumption, investment, export data and electricity consumption is studied. Firstly, mathematical model of “Troika” leading to electricity consumption is built. Secondly, by utilizing input-output tables in 2002, 2007 and 2012 published by Statistics Bureau of Fujian and industrial electricity consumption tables in Fujian Province published by State Grid Fujian Electric Power Company, relationship and developing trends between electricity consumption and “Troika” are calculated. Study results show that pushing effect of investment to electricity consumption increases year by year, while that of consumption decreases. The pushing effect of export is the smallest. From industrial views, the electricity consumption caused by other manufacturing industry accounts for the biggest proportion, while that caused by finance represents the smallest percentage.

Compared with the AC distribution network, the DC distribution network has the advantages of lower line losses, better power quality, higher reliability and being suitable for all kinds distributed generation and DC load access, etc. This paper firstly studies the models of PV and energy storage connected to the DC distribution network. Then, a new type of DC distribution network topology with hybrid DC circuit breaker is proposed. A coordinated control strategy is also proposed, which can control energy storage to improve the stability of the system. Finally, using PSCAD/EMTDC to model and simulate. The simulation results show that the proposed topology of DC distribution network and control strategy can quickly remove the fault, shorten the fault power failure time and ensure the safe and stable operation of the system, which greatly improves the operation efficiency and safety level of the power grid.

The algorithm clustering by fast search and find of density peaks shows good efficiency and accuracy, but the space complexity of the algorithm is too high since it has to keep a global distance matrix in memory, so it can hardly process big dataset clustering. To solve this problem, this paper designed a new strategy for the algorithm to search the important quantity δ, by using the new strategy, the space complexity of the algorithm is greatly reduced. And based on that reduction, a corresponding load balanced parallel clustering algorithm was presented in this paper, experimental results show that the parallel algorithm is efficient and scalable.

A metamodel is a description language of models. To address the problem that validation rules of the SMP2 metamodel haven't been implemented by XML Schema, a method to enhance the accuracy of the model by adding static semantics to the SMP2 metamodel is proposed. Supposing to use Java language or OCL for constraints definition and use EMF for the validation of static semantics of SMP2 metamodel, two methods to validate static semantics of models based on Eclipse modelling framework, namely EMF Ecore validation and EMF validation framework extension, are analyzed in this paper. Java language and OCL are employed to implement constraints definition. Constraints specified formally based on OCL are executed via an OCL engine. On the basis of both validation methods, SMP2 metamodel constraints are to be described, modeled and validated using EMF validation methods.

Zigbee-based wireless sensor network technology attracted widespread attention in various fields, unknown nodes location measurement in the wireless network even become a hot topic. The traditional method about the unknown nodes location measurement is based on RSSI, or Two-phase positioning cycle refinement. This paper presents an improved algorithm of the cycle of refinement based on RSSI location algorithm for least squares estimation. The weight of node was introduced on the base of the traditional cycle of refinement algorithm, and discard the node whose value of weight is outside the minimum weight, this algorithm use the weighted triangle center of gravity algorithm in the refinement stage. minimize the influence which was brought by the nodes whose measurement error were large. Simulation results show that errors exist by using this algorithm , but more accurate than traditional algorithms.

Within a net centric environment, military communication networks (MCNs) are mostly characterized by heterogeneous nodes and multilinked relationships between nodes. Traditional graph theory based network structure models are difficult to describe such characteristics of MCN accurately. In order to analyze, design, and build MCNs for a net centric environment, we propose a novel structure model based on super network by extending and integrating Jeff Cares' combat network model and Hans E.keus' Net force Reference Model. The model's mathematical structure is given in detail. The proposed model is able to represent two key characteristics of MCNs, i.e. node's functional types and link's transmission types. Based on the proposed model, the military communication super network can be decomposed into several functional or special sub-networks which are more meaningful in the military background. As an example, a joint operational forces communication super network structure is given to validate the proposed super network model.

The existing Timed Influence Nets (TIN) framework is assumed that delays on arcs are constant. This constraint may turn out to be unrealistic in many real world situations. The proposed parametric enhancements would overcome the above limitation, and enable a system modeler to specify stochastic delay in a dynamic uncertain situation that the existing TIN fails to capture. The new class of models is named Stochastic Timed Influence Nets (STIN). Both TIN and STIN provide an easy-to-read and compact representation to several time-based probabilistic reasoning paradigms.

One of the main reasons of lag in braking control technology of hoist exchange drag is that braking torque is not adjustable. And the braking torque of speed closed-loop of fuzzy neural network controller (NNSOC) can not only ensure that deceleration in braking is constant, but also it can be regulated in the wider framework. The coil current of nozzle flapper valve of pressure closed-loop of NNSOC changes with the change of braking torque. Mze–I characteristic curve of the valve was simulated to obtain the braking torque expectation (Mze). NNSOC adopted 3-layer structure of BP network, and network input / output samples were based on fuzzy rules and collected by simulation test. After doing repeated experiments, the experimental results show that that traingdx learning function and the best network structure can adjust network weights to memory and update the rules of NNSOC. The network testing results show that the response characteristics of NNSOC (braking torque and its overshoot of control current, network response time) can ensure that the braking deceleration is constant and the braking is smooth, safety, utility, energy-saving.

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too