Regarding the intelligent network management system, the traditional systematic sampling method is unable to monitor to the modern information network in real-time. In order to solve this problem, this paper presents a fuzzy self-adaptive sampling method. Based on the fuzzy control principle, the method can dynamically adjust the sampling interval using the set of membership functions and fuzzy rules, and it can detect the network anomaly and network bottlenecks in the minimize of affecting network latency and network bandwidth. Experimental results show that this method is less than the system sampling method 69% based on the same error condition, and the errors of fuzzy adaptive sampling method are less than the system sampling method 54% in the equal sample count.
Dozens or even hundreds of virtual routers may be deployed in a memory limited physical router. To save memory overhead, this paper proposes an algorithm named optimal trie merging algorithm. It adopts dynamic programming approach to find the nodes for initial merging of each trie, and computes the number of nodes of the optimal trie. The sub-node arrangements of any two nodes, which achieve optimal matching, are written down in the process of dynamic programming computation. Then, according to the three computation results, it constructs a data structure named optimal trie. Experimental results show that the algorithm saves 20%~90% memory overhead than simple trie merging algorithm.
This paper designs a path planning system for crane lifts. The problem of lift path planning is formulated. The architecture of this system is proposed. The overview of path planning approaches is focused on for crane lifts. The paper introduces the implementation of path planning module. Experimental results show that this system is able to visualize the result of one certain path planning algorithm and compare the performance of several algorithms under the same condition. Besides, this system allows users not only easily to append new algorithm, but also handily to add new lift path planning problem.
A novel Artificial Fish Swarm Algorithm(AFSA) based on differential evolution is proposed, which aims to accelerate convergence and improve accuracy of AFSA, and refers to the strategy of globel fish swarm cluster and trace action. The fish cluster for the whole fish center and trace with the bulletin board record in the algorithm. Meanwhile, it is set stagnation threshold and stagnation record in the bulletin board so that fish can execute the differential evolution for out of local minima in the stagnation stages and overcome the lack of purpose of the fish search by it. The convergence and accuracy of the algorithm are improved significantly after evolution. Comparing to the results of other AFSAs and Particle Swarm Optimization(PSO), result shows that the algorithm has better optimization effects.
The method of distance measurement system based on single Duffing system has large system error and determining system state is complex. Aiming at these problems, this paper proposes a novel method of ultrasonic distance measurement based on Duffing oscillator coupled synchronization system. It adopts the coupled synchronization system to receive the echo signal, and according to the output signal of the synchronization system hop or not to determine whether the echo signal arrives, so that the method can achieve accurate ranging. By using the system’s property of being immune to noise, it can not only reduce the effect of noise, but also improve the measuring distance, and determining system state is simple. Simulations are given to verify the feasibility of the proposed method.
A recent path loss measurement is presented in two typical kinds of outdoor urban application environment for Wire- less Sensor Network(WSN) channel model in three different frequency channels. The measured data are analyzed by least square linear regression method. The results indicate that radio channel is different from the traditional one, but two slope log distance path loss models are still acceptable. The analysis outcome can provide the evidence of design and engineering application for WSN in urban application environment.
In order to enhance efficiently a degraded video image in various scenes, a new adaptive image enhancement algorithm is proposed. The new algorithm uses Laplacian operator for extraction of image detail, constructs and uses different grey-level conversion strategy for images in different scenes based on dynamic scene estimation, and adaptively adjusts image’s gray-level range to improve the performance of image enhancement. By using line buffer and pipeline structure, the algorithm has a good timing performance and compact hardware implementation with minimum use of memory resource. Experimental result shows that the proposed algorithm has satisfying performance in each scene, while has a careful protection of image’s original details.