Shunting Equation Research Articles

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Modeling the impacts of assumptions and nonpulmonary factors on the performance and reliability of indices of oxygenation.

Assessment of oxygenation is fundamental to the care of patients. Numerous indices of oxygenation have been developed that entail variable degrees of invasiveness, complexity and physiologic underpinning. The clinical reliability of these indices has been questioned. This theoretical study uses a steady-state model of blood gas physiology to study the assumptions and nonpulmonary factors that have been hypothesized to impact index performance. A model derived from cardiac and pulmonary Fick expressions was used to calculate the effects of the physiological parameters-shunt, dead space, cardiac output, ventilation, oxygen extraction, carbon dioxide elimination, hematocrit, temperature and base excess-on predicted arterial, mixed-venous and post-capillary oxygen contents and arterial and alveolar oxygen and carbon dioxide partial pressures. Values of these parameters were determined over a range of shunt from 0 to 50% and then used to calculate (1) estimated shunt with the shunt equation and FShunt, and (2) the alveolar-arterial partial pressure of oxygen difference (A-a [Formula: see text] gradient), and the arterial partial pressure of oxygen to fraction of inspired oxygen (Pa/Fi) ratio. Calculations were performed either treating parameters as fixed (assuming several values) or as random variables. Assumptions of constant arterio-venous oxygen content and of alveolar and arterial partial pressures of carbon dioxide being equal were shown to fail in certain settings where shunt and physiologic parameters varied. These effects manifested as calculated indices either over or under-estimating actual shunt by FShunt, or wide unpredictable variability (scatter) when correlating A-a [Formula: see text] gradient and Pa:Fi ratio to actual shunt. Cardiac output and oxygen extraction have noticeable impacts on all calculated indices. The results support the clinical observations that the performance of indices of oxygenation can vary with fraction of inspired oxygen and various nonpulmonary physiological factors that underly heterogeneity present in the clinical population.

Estimation of intracardiac shunts in young children with a novel indicator dilution technology

Clinical evaluation of intracardiac shunts in children is not straightforward. Echocardiography can only diagnose the presence of a shunt but does not estimate the shunt ratio. This can be a critical factor that influences treatment options. In this single-center, prospective, observational, method-comparison study, we validate the ability of a novel monitoring device COstatus to estimate the intracardiac shunt ratio (Qp/Qs) of pulmonary (Qp) to systemic (Qs) blood flow in young children before and after corrective cardiac surgery. The indicator dilution technology COstatus monitor was compared to two other more invasive reference techniques, perivascular ultrasonic flow probes (placed around the pulmonary truncus and ascending aorta) and the oximetric shunt equation (using arterial and venous blood gases). Our study revealed that the COstatus monitor detected intracardiac shunts with high sensitivity and specificity but there was some underestimation of the shunt ratios compared to the reference techniques.

The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer) inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.

AUV Guidance System for Dynamic Trajectory Generation

This paper considers an AUV guidance system for collision-free transit and complete area coverage. A topologically organized neural network model is used to represent the operating environment. The activity of each neuron, characterized by a shunting equation, is used to represent the local environmental information. Targeted areas have the highest values. The AUV moves from areas with low activity to areas with higher activity like in a potential field navigation. The kinetic constraints of the AUV are taken into account by using Dubins theory to generate feasible paths.

VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS

In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.

Physics, Pharmacology and Physiology for Anaesthetists

M. Cross and E. Plunkett Cambridge University Press , ISBN 978-0-521700-44-3 , 248 pp. Price £27.99 (US$56.00 ) In this new addition to the catalogue of FRCA revision books, the authors have endeavoured to pull together the key basic science concepts that form the major part of the primary, and to a lesser extent the final, examination. This is a clearly presented and well laid-out book, with chapters covering physics, pharmacology and physiology as expected. In addition, there are excellent sections on mathematical principles and statistics plus a useful appendix containing tables of the physical/pharmacological properties of anaesthetic agents. In the introduction, the authors make it quite clear that this book is to be used as a revision aid, particularly aimed at the viva part of the examinations, and is not intended to be a definitive or comprehensive textbook on the basic sciences relevant to anaesthesia. This is a useful distinction, which can often be overlooked by the trainee when choosing which textbooks to purchase. The first four pages, while not really containing any hard facts about the basic sciences, are perhaps the most useful of the book, as they set the tone for the forthcoming chapters. The focus on these pages is a practical and logical approach to answering viva questions, with the emphasis on being able to quickly and accurately draw graphs and pictorial representations pertinent to the question being answered. The thrust of the book is then immediately clear; the reader is not being taught the finer points of respiratory physiology, but rather being shown how best to answer a viva question about dead space. Each chapter is divided into subsections, usually a couple of pages long, discussing a basic theme, such as the gas laws, shunt equation, or pulse oximetry. In each case, clear diagrams, equations and definitions are presented, with tips on how to reproduce accurate graphs, as well as pitfalls to avoid when answering questions. The breadth of the subject matter is wide, and this book is particularly useful as it has chapters on all three major basic science subject areas. There are also sections on topics such as pain, monitoring of neuromuscular block, calibration, and pharmacokinetics – subjects that are not always found together in one revision book. Despite its breadth of coverage and its clear, exam-oriented focus, this book is not a substitute for the weightier tomes of formal textbooks. The candidate who struggles with the derivation of the shunt equation is unlikely to find enlightenment here. This book is, after all, a revision guide, and it is assumed that the topics have been mastered earlier in the process of exam preparation. Similarly, there are topics that are not addressed; although capnography and pulse oximetry are covered, there is no section on gas and vapour analysis, for example. This is only a minor quibble – a revision guide must by necessity be focused and succinct, otherwise it becomes no more useful than a larger textbook. These pages represent the pared down revision notes that an extremely well prepared candidate may have made over many months of exam preparation – clear, distilled, rich in graphs and diagrams, and focused towards quickly and accurately conveying that information in the viva situation. Drs Cross and Plunkett have produced a most useful book and we commend it to all those facing the FRCA.

A recurrent model of contour integration in primary visual cortex

Physiological and psychophysical studies have demonstrated the importance of colinearity in visual processing. Motivated by these empirical findings we present a novel computational model of recurrent long-range processing in the primary visual cortex. Unlike other models we restrict the long-range interaction to cells of parallel orientation with colinear aligned receptive fields. We also employ a recurrent interaction using modulatory feedback, in accordance with empirical findings. Self-normalizing shunting equations guarantee the saturation of activities after a few recurrent cycles. The primary computational goal of the model is to evaluate local, often noisy orientation measurements within a more global context and to selectively enhance coherent activity by excitatory, modulating feedback. All model simulations were done with the same set of parameters. We show that the model qualitatively reproduces empirical data of response facilitation and suppression for a single bar element depending on the local surround outside the classical receptive field (M. K. Kapadia, M. Ito, C. D. Gilbert, & G. Westheimer, 1995). Next we evaluate the model performance for the processing of artificial and natural images. We quantitatively evaluate the model using two measures of contour saliency and orientation significance. We show that both measures monotonically increase during the recurrent interaction and saturate after a small number of recurrent cycles. The model clarifies how basic tasks of early vision can be accomplished within a single, biologically plausible architecture.

Biologically Inspired Neural Computation for Ginseng Drying Rate

Drying rate is one of the most important characteristics of drying, reflecting the complex effect of operating and initial conditions, such as air temperature, humidity, air velocity and root size. The computation of drying rate is very difficult because of the non-linearity and coupling effects in heat and mass transfer. It is especially difficult for fresh ginseng, where drying rate depends on spatio-temporal adaptation of ginseng root to drying conditions. In this paper, a biologically inspired neural-network approach was proposed for modelling of the drying rate of ginseng. The simulation of the ginseng drying rate through a shunting equation showed a good fit with the experimental data under various operating conditions and root categories. In addition, the shunting equation takes into account the spatio-temporal adaptation of large roots, providing good prediction for a wide range of root sizes. Due to the possibility of physical interpretation of model parameters, the shunting model can be used as the state estimator of the control system to improve observability and controllability of ginseng drying.

A Biological Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot

In this paper, a novel biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment. The real-time collision-free robot trajectory is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation derived from Hodgkin and Huxley’s biological membrane equation. The dynamically changing environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject to the nonholonomic kinematic constraint of the mobile robot. The tracking velocities for the mobile robot are generated by a novel neural dynamics based controller, which is based on two shunting equations and the conventional backstepping technique. Unlike the conventional backstepping controllers suffer from sharp jumps, the proposed tracking controller can generate smooth and continuous commands. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.

Perspectives of Computational Intelligence in Robotics and Automation

Perspectives of Computational Intelligence in Robotics and Automation

Noninvasive measurement of intrapulmonary shunting

Objective: To investigate the accuracy and precision of a noninvasive approach to measurement of pulmonary shunt fraction using simultaneous application of 2 fundamental respiratory mixing equations: the direct Fick equation for oxygen and the shunt equation of Berggren. This can be performed without mixed venous blood sampling and requires measurement of oxygen uptake and pulmonary blood flow. Design: Comparison with invasive shunt fraction measured using mixed venous blood sampling and with estimated shunt fraction using an assumed arteriovenous O 2 content difference. Setting: Major teaching hospital. Participants: Nine patients undergoing anesthesia for cardiac surgery. Interventions: Pulmonary blood flow was measured using an indirect Fick technique (nitrous oxide throughflow) and by bolus thermodilution for comparison. Measurements and Main Results: The mean shunt fraction measured by the invasive method was 0.145 (range 0.057–0.263). When pulmonary blood flow was measured using an indirect Fick technique (nitrous oxide throughflow), the absolute mean bias for noninvasive shunt fraction was −0.005 with a standard deviation of 0.012. Correlation was excellent ( r 2 = 0.95, p < 0.001). Agreement was less precise when pulmonary blood flow was measured using thermodilution (mean bias + 0.001 with a standard deviation of 0.018). Conclusions: The noninvasive method is an accurate substitute for invasive shunt fraction measurement with mixed venous blood sampling.

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.

Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

A NEURAL COMPUTATION MODEL FOR REAL-TIME COLLISION-FREE ROBOT NAVIGATION

A biologically inspired neural computation model is proposed for dynamic planning and tracking control of robots. The dynamic environment is represented by a neural activity landscape of a topologically organized neural network, where each neuron is characterized by a shunting equation. The collision-free path is generated in real-time through the activity landscape without any prior knowledge of the dynamic environment. The real-time tracking control of robots to follow the planned path is also designed using shunting equations. The effectiveness is demonstrated through case studies. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach.

Neural network approaches to dynamic collision-free trajectory generation

In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

An efficient neural network approach to dynamic robot motion planning

In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

Evaluation of a mathematical model to predict intrapulmonary shunt non-invasively.

We have previously published a mathematical model of oxygen transport. Using several physiological assumptions, the model provides a non-invasive estimate of intrapulmonary shunt. During a larger study of lung injury in a pig model, we had the opportunity to check the validity of our assumptions and the accuracy of the model's predictions. We used six female pigs, average weight 12.8 kg. Following general anesthesia, tracheostomy and insertion of pulmonary venous and arterial lines, lung injury was induced by repeated saline lung lavage. Using hemodynamic measurements made at different levels of inspired oxygen, intrapulmonary shunt was calculated both by the traditional shunt equation and also by our mathematical model based on non-invasive measurements of FIO2 and SaO2. There was good agreement between the two methods of shunt calculation. Using linear regression the correlation coefficient was 0.95. Bland and Altman analysis showed a bias of -0.8 and precision of 12%. In a controlled setting, intrapulmonary shunt can be estimated from non-invasive measurements to a reasonable degree of accuracy. However, the calculation requires too many assumptions to be of general clinical value. The equations used provide a validated physiological model that acts as a useful tool for teaching cardiorespiratory physiology.

DECREASED CO2 PRODUCTION AND INCREASED SHUNTING PROLONGS TIME TO EXTUBATION IN MVR PROCEDURES

Introduction: Respiratory dysfunction is a major cause of morbidity following cardiopulmonary bypass (CPB). To assess the relation between shunting (Qs/Qt), CO2 production (VCO2), and time to extubation, we studied patients undergoing both MVR and CABG procedures. Methods: Following IRB approval, we prospectively enrolled 16 patients undergoing elective MVR (for mitral insufficiency) and CABG procedures (MVR=6, CABG=10). VCO2 was measured at baseline (pre-CPB) and at 1, 2, and 4 hours post-CPB employing the CO2 SMO-plus monitor (I.R. capnography with a mainstream flow sensor, Novametrix Inc., Wallingford, CT). Qs/Qt was calculated using the shunt equation. All patients had an EF >or=to45% and no significant pulmonary disease. All measurements were obtained after induction of general anesthesia, under mechanical ventilation, and with the chest cavity closed. Demographic data (age, body suface area (BSA), body temperature), hemodynamic profiles (HR, MAP, CO, mean PA pressure) and CPB factors (duration, cooling temp (Tc), volume of cardioplegia), and total intraoperative iv fluids were also collected. Statistical analyses included paired and unpaired t-tests and correlation Z-tests (95% CI). Results: Age was negatively correlated with VCO2 measurements in both the MVR and CABG groups. MVR procedures are associated with more shunting both before and after surgical repair (Table 1). VCO2 was negatively correlated to the duration of intubation at all times in the MVR group (Table 2). VCO2 was also positively correlated to end-tidal CO2 and to BSA early after CPB in both groups.Table 1: VCO2 vs Duration of Intubation in MVR proceduresTable 2: VCO2 increases as Qs/Qt decreases over time in MVRConclusions: This shunting is inversely related to CO2 production, and is particularly significant as age increases. Lower VCO2 following CPB may account for the increased duration of postoperative ventilatory support.