Biomedical subjects

Simon X Yang

Publications and source records attributed to Simon X Yang.

6 recordsLinked to original sources

PubMed2006

A neural network approach to dynamic task assignment of multirobots.

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

Algorithms↗

PubMed2006

An efficient dynamic system for real-time robot-path planning.

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.

Algorithms↗

PubMed2005

A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7.

An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the inputs to a three-layer back-propagation neural network. The ANN model was trained using the data-set of McKellar et al. [2002. A probability model describing the interface between survival and death of E. coli O157:H7 in a mayonnaise model system. Food Microbiol. 19, 235-247] that consisted of 1,820 treatment combinations from controlled experiments with a cocktail of five strains of E. coli O157:H7. After training, the model correctly predicted the growth/no-growth in 1,810 combinations (99.5%) with 8 false positives and 2 false negatives, and survival/death in 1,804 combinations (99.1%) with 13 false positives and 3 false negatives. Classification accuracy was validated using additional literature data-sets for growth of E. coli O157:H7 under various environmental conditions. The ANN model accurately predicted the survival/death in 27 of 30 cases (90%) in experimental mayonnaise inoculated with E. coli O157:H7, with 3 fail-positive predictions and all observed growth (100%). Simulations were used to estimate the influence of incubation temperature on survival and growth for specific combinations of acetic acid, salt, pH and sucrose. The ANN model is recommended as an alternative tool for classification of survival and growth conditions in predictive microbiology.

Colony Count, Microbial↗

PubMed2004

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.

Journal Article↗

PubMed2003

Neural computations in the tiger salamander and mudpuppy outer retinae and an analysis of GABA action from horizontal cells.

A neural network architecture based on the neural anatomy and function of retinal neurons in tiger salamander and mudpuppy retinae is proposed to study basic aspects of early visual information processing. The model predictions for the main response characteristics of retinal neurons are found to be in agreement with neurophysiological data, including the antagonistic role of horizontal cells in the outer plexiform layer. The examination of possible gamma-aminobutyric acid (GABA) action from horizontal cells suggests that GABA(A) alone, GABA(B) alone, or their weighted combination can generate the response characteristics observed in bipolar cells.

Ambystoma↗

PubMed2002

Relationship between pain and vertebral motion in chronic low-back pain subjects.

OBJECTIVES: To investigate the relationship between intervertebral motion, intravertebral deformation and pain in chronic low-back pain patients. DESIGN: This study measured vertebral motion of the lumbar spine and associated pain in a select group of chronic low-back pain patients as they performed a standard battery of motions in all planes. BACKGROUND: Numerous studies have demonstrated that individuals with low-back pain have impaired spinal motion, yet few studies have examined the specific relationship between pain and motion parameters. Although it is accepted that the pain in mechanical low-back patients is due to specific spinal motions, no studies have related specific motions to pain measures. METHODS: Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. The external fixator frame was removed following the clinical external fixation test. The 3D locations of the pedicle screws and the level of pain were recorded as the subjects performed a battery of motions. The relationship between the pain and motion parameters was assessed using linear discriminant analysis and neural network models. RESULTS: The neural network model showed a strong relationship between observed and predicted pain (R(2)=0.997). The discriminant analysis showed a weak relationship (R(2)=0.5). CONCLUSIONS: Vertebral motion parameters are strongly predictive of pain in this select group of chronic low-back pain patients. The nature of the relationship is nonlinear and involves interactions; neural networks are able to effectively describe these relationships. RELEVANCE: Specific patterns of intervertebral motion and intravertebral deformation result in pain in chronic low-back pain patients. This substantiates the mechanical back pain aetiology.

Adult↗