Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

A self-organized locator obtaining method driven by local minima

The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today.Greedy routing,in which each node is assigned a locator used as a distance metric,recently received increased attention from researchers and is considered as a potential solution for scalable routing.In this paper,LMD—a local minimum driven method is proposed to compute the topology-based locator.To eliminate the negative effect of the " quasi" greedy property—transfer routes longer than the shortest routes,a two-stage routing strategy is introduced,which combines the greedy routing with source routing.The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage.Through extensive evaluations,based on synthetic topologies as well as on a snapshot of the real Internet AS(autonomous system)topology,it is shown that LMD guarantees 100%delivery rate on large networks with low stretch.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Epidural fentanyl decreases the minimum local analgesic concentration of epidural lidocaine

Background Epidural lidocaine can be used when regional anesthesia needs to be established quickly, but the effect of co-administering epidural fentanyl on the minimum local analgesic concentration （MLAC） of lidocaine is not known. We compared the MLAC of epidural lidocaine in combination with different doses of fentanyl for epidural anesthesia in adults. Methods One hundred and twenty patients requiring epidural analgesia were randomly allocated to receive 20 ml of one of four solutions： lidocaine, or lidocaine plus fentanyl 1 pg/ml, 2 pg/ml, or 3 pg/ml. The first patient in each group was administered 1% lidocaine weight by volume; subsequent patients received a concentration determined by the response of the previous patient to a higher or lower concentration according to up and down sequential allocation in 0.1% increments. Efficacy was assessed using a visual analog pain scale, and accepted if this was 〈10 mm on a 100 mm scale within 30 minutes. The extent of motor block and of nausea and vomiting were recorded at 30 minutes after administration of the epidural solution and two hours after surgery, respectively. Results The MLAC of lidocaine in those receiving lidocaine alone was 0.785% （95%C/0.738-0.864）. A significant dose-dependent reduction was observed with the addition of fentanyl： the MLAC of lidocaine with fentanyl at 2 pg/ml was 0.596% （95%C/0.537-0.660） and 0.387% with fentanyl at 3 pg/ml （95%C/0.329-0.446, P 〈0.001）. Conclusion Epidural fentanyl significantly reduces the dose of lidocaine required for effective epidural analgesia in adults without causing adverse side effects. （Chi CTR-TRC-11001559）

Genetic Algorithm Based on New Evaluation Function and Mutation Model for Training of BPNN

A local minimum is frequently encountered in the training of back propagation neural networks (BPNN), which sharply slows the training process. In this paper, an analysis of the formation of local minima is presented, and an improved genetic algorithm (GA) is introduced to overcome local minima. The Sigmoid function is generally used as the activation function of BPNN nodes. It is the flat characteristic of the Sigmoid function that results in the formation of local minima. In the improved GA, pertinent modifications are made to the evaluation function and the mutation model. The evaluation of the solution is associated with both the training error and gradient. The sensitivity of the error function to network parameters is used to form a self adapting mutation model. An example of industrial application shows the advantage of the improved GA to overcome local minima.

Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim

Mobile robot path planning is an important research branch in the field of mobile robots.The main disadvantage of the traditional artificial potential field(APF)method is prone to local minima problems.Improved artificial potential field(IAPF)method is presented in this paper to solve the problem in the traditional APF method for robot path planning in different conditions.We introduce the distance between the robot and the target point to the function of the original repulsive force field and change the original direction of the repulsive force to avoid the trap problem caused by the local minimum point.The IAPF method is suitable for mobile robot path planning in the complicated environment.Simulation and experiment results at the robot platform illustrated the superiority of the modified IAPF method.

Simulation of ATPG Neural Network and Its Experimental Results

This paper first establishes a neural network model for logic circuits fromthe truth table by using linear equations theory, presents a kind of ATPG neuralnetwork model, and investigates energy local minima for the network- And then,it proposes the corresponding techniques to reduce the number of energy localminima as well as some approaches to escaping from local minimum of eliergyFinally, two simulation systems, the binary ATPG neural network and thecontinuous ATPG neural network, are implemented oli SUN 3/260 workstationin C language. The experimental results and their analysis and discussion aregiven. The preliminary experimental results show that this method is feasibleand promising.