Earticle

This paper is focused on the method of ultra-high precision frequency measurement.Based on comparing the phase characteristic of standard frequency and measured frequency, it proposes the relationship between group phase-shift and quantitative phase-shift resolution when existing frequency drift. The relationship among measurement accuracy, trigger pulse position and the pulse number is set up when the measurement gate is formed according to the group period.Through the programmable counters, the pulses of triggering gate are reduced. The application of nonlinear circuit compresses and expandes trigger pulse, which improves the accuracy of triggering gate. Frequency measurement system was designed using FPGA.Experiments showed that measurement accuracy can reach 10-12 . The system can be used for ultra-high precision measurement of frequency .

The main reasons of diesel engine load under speed can be summed up as valve spring fracture, nozzle coking blocked hole, engine oil pipe fracture, fuel injection delay, fuel injection pump drive roll key. There are six symptoms. Using the symptoms and the reasons establishes fuzzy corresponding relation, establishing the automatic fault diagnosis fuzzy model of diesel engine load under speed, through the symptoms of diesel engine load under speed to judge its fault reasons, which could treat the faults quickly so as to ensure the normal operation of the diesel engine.

Flow mobility distribution for mobile devices is essentially important for IP-based environments in order to efficiently manage the load of data traffic. For Heterogeneous hierarchical mobility management protocol, the load of traffic is concentrated on the mobility anchor point (MAP) for it acts as the central controller for all connecting mobile devices. Whenever, the MAP handoff is breached, the entire operation that goes through the MAP is compromised. This paper deals with the analysis of a secured distribution of traffic load within the MAP domain over hierarchical mobility management environments. The overview of hierarchical mobility management will be discussed as well as securing the flow control signaling scheme will be introduced in order to determine and ensure the optimized flow of traffic, considering the amount of traffic that every MAP handles.

In this paper, a new model, core vector regression (CVR) optimized by memetic algorithm (MA), is presented to predict electric daily load. Support vector regression (SVR) has obtained wide focus in recent years to solve nonlinear regression problems in many fields. However, it is limited on large scale dataset problem because of its high time and space complexity. Hence, CVR is proposed to improve the SVR on solving large scale dataset problem. Proper parameters selection of CVR model determines the complexity and accuracy of the model. In this paper, MA is proposed to optimize the parameters of CVR, which is called MA-CVR. Electric load is the time-dependent data which shows recurrent pattern weekly, seasonally and yearly. In this paper, we adopt MA optimization method and choose adaptive parameters dynamically based on time recurrent character of electric load data. Experimental results show that MA-CVR outperforms the existing model optimized by genetic algorithm which is called GA-CVR.

The technological advancements in process monitoring, control and industrial automation have played a decisive role in increasing the industrial productivity and manufacturing at faster pace even than been dreamed. An ultimatum requirement is an accurate and precise data acquisition mechanism which can be executed through diverse sensors especially for collecting, analyzing and sorting the objects and elements. In this work, an energy efficient conveyor system model is proposed and implemented which not only recognizes and sorts the objects by sensing its colour and place these objects to its destination by using Robotic vehicle but also smartly adjusts the speed of conveyor belts by recognizing the weight of object(s). The variable speed drive (VSD) based an optimal belt speed control mechanism is proposed by smartly sensing the object weight and optimally adjusting the belt speed. The proposed system optimally switches the conveyor system to on/idle/off status to minimize the energy consumption of conveyor belts. In this work, a mathematical model of the energy efficient conveyor system is also derived by considering the different dynamic parameters. When conveyor belt is fully loaded with objects, the belt moves around with its maximum potential speed, but when conveyor belt is partially or marginally loaded or unloaded, the speed of belt is adjusted accordingly. In this way, a significant amount of energy and cost of energy can be saved. It is anticipated that, the developed intelligent energy efficient conveyor system model will not only modernize the industrial manufacturing and distribution process but will significantly reduce the energy consumption and cost and will lead to increase the life time cycle of conveyor belts.

The building of distributed power and smart grid accelerates the development of intelligent contactor. The design of intelligent contactor has gradually attracted more attention. This review introduces the latest research progress of intelligent contactor including four aspects such as intelligent design and optimization, no-arc control technology, less bounce control technology and subsidiary structure and function. This review focuses on power electronics technology, artificial intelligence content algorithm, and feedback control and so on .Some ideas are proposed to be further studied. Finally, the intelligent contactor’s development is looked into with the future of China's energy management pattern.

This paper uses the image entropy directly on the color image to express the integral color image information and color spatial distribution of the neighborhood, then the image is divided into blocks, for each block we use the Pelaez Aggregation to determine the seed, after that we use the regional expansion method for color image blocks to extract the color characteristic of color image. The centroid was extracted from the segmented image and combined with variance as the similarity metric standard. Result shows that, this method is not only simple and efficient, but also improves the performance of image retrieval.

Full-vehicle model plays a key role in vehicle handling stability simulations, and the same vehicle model does not adapt to all kinds of vehicle simulations. In the thesis, the vehicle mechanisms of an 8×4 type construction truck were appropriately simplified and the full-vehicle parameterized multi-body dynamic model was developed according to the topology mechanical map of each part. The vehicle model suitability was researched through comparing the vehicle simulation results with the test results in lemniscate and returnability simulations. The comparison results show that the lemniscate simulation errors of the steering wheel angle, yaw angle velocity and lateral acceleration amplitude of the cargo mass centre are 3.9%, 4% and 4.4% respectively, and the returnability simulation errors of the residual steering wheel angle, residual yaw angle velocity and lateral acceleration of the cargo mass centre are 95%, 77.8% and 80% respectively. The simulation and test results all show that the lemniscate simulation errors are smaller than the returnability simulation errors because the model input was the steering wheel angular displacement in lemniscate simulation while the model input was the forces caused from the road and vehicle movement in returnability simulation. In brief, there exist significant error variations for the same vehicle model in different simulations.