Changes In System Dynamics Research Articles

Nonsurgical management of spinal stenosis: how evidence-based are we?

To the Editor, I was interested to read the article by Tran et al. published in the July 2010 issue of the Journal. The authors are to be credited for dealing with the challenges of determining the current best evidence for nonsurgical interventions for lumbar spinal stenosis (LSS). However, based on limitations of the cited publications, the conclusions that can be drawn are limited. Regardless of how the evidence was compiled, if the underlying studies themselves are flawed, the resultant reviews based on that evidence are limited with respect to the conclusions and inferences that can be drawn for treating patients with this unfortunate pain complex. Lumbar spinal stenosis is a clinical syndrome of buttock or leg pain, with or without back pain, and it is associated with decreased lumbar spinal canal space. There are no specific criteria to diagnose this condition. The heterogeneity of LSS symptoms has been well analyzed. Clinically, neurogenic claudication (NC) is considered to be a specific feature; the frequency of NC varies from 23% to 94% depending on the selection criteria and the definition used. Pain observed with NC is often poorly localized with no correlation to central or lateral canal narrowing. In the study by Goh et al., 62% of patients had back pain and 61% of patients had NC. Predominant back pain has other possible diagnoses, including spondylolisthesis, which is considered by many to be a confounding factor that should be excluded from the analyses. In the review by Tran et al., no study was excluded on the basis of the definition of intervention allocation or primary and secondary outcomes. Only three of the reported studies included under epidural anesthesia used fluoroscopic guidance. Incorrect needle placement with non-fluoroscopically-guided epidural analgesia could be as high as 30%. In two studies, neurogenic claudication was not considered for inclusion criteria. Cuckler et al. reported a lumbar epidural approach without fluoroscopy. Surprisingly, a second epidural was given for ethical reasons and not technical failures. In the only positive study evaluating calcitonin, a crossover design was used after a two-month washout period. Findings included a decrease in pain scores for three months. In Group 1 (placebo), there was a trend towards decreased pain scores, even after three months. It is difficult to evaluate the pharmacodynamics of a medication in patients with chronic pain, especially with drugs having unknown mechanisms of action, including epidural steroids. Even in patients with LSS, prolonged effects are possibly associated with changes in central nervous system dynamics. The guidelines advanced by Watters et al. seem to differ from those published in the review by Tran et al., both for calcitonin and epidural steroids.

Real-Time HIL Closed Loop System of Rolling Mill’s Electro-Hydraulic Gap Adjustment

Hardware-in-the-loop (HIL) test is a good ground for examine the dynamic model of rolling to observe the movement of the mill and monitor dynamic behavior of the hydraulic system and the mill. Nowadays hydraulic actuators/capsules are used to control the gap in rolling mills which determines the strip thickness, profile and flatness. Using HIL simulation, dynamic parameters of rolling mill stand and hydraulic gap adjustment system can be tested together in realtime. It has possibility to examine the dynamic phenomena inside the mill stand and hydraulic system using trials to test how system dynamic behavior changes when system’s parameters are shifted. In this paper a HIL real-time model was developed and realized to test the dynamic behavior of the complete rolling mill system which enables us to observe the system’s parameter variations for example when seals are worn out in the hydraulic actuator.

Optimal Management with Potential Regime Shifts

We analyze how the threat of a potential future regime shift affects optimal management. We use a simple general growth model to analyze four cases that involve combinations of stock collapse versus changes in system dynamics, and exogenous versus endogenous probabilities of regime shift. Prior work has focused on stock collapse with endogenous probabilities and reaches ambiguous conclusions about the effect of potential regime shift on optimal management. We show that all other cases yield unambiguous results. In particular, with endogenous probability of regime shift that affects system dynamics the potential for regime shift causes optimal management to become precautionary.

Optimal Management with Potential Regime Shifts

We analyze how the threat of a potential future regime shift affects optimal management. We use a simple general growth model to analyze four cases that involve combinations of stock collapse versus changes in system dynamics, and exogenous versus endogenous probabilities of regime shift. Prior work in economics has focused on stock collapse with endogenous probabilities and reaches ambiguous conclusions on whether the potential for regime shift will increase or decrease intensity of resource use and level of resource stock. We show that all other cases yield unambiguous results. In particular, with endogenous probability of regime shift that affects system dynamics the potential for regime shift causes optimal management to become precautionary in the sense of maintaining higher resource stock levels.

Structured decision making as a conceptual framework to identify thresholds for conservation and management

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds.

The Effects of Computational Method, Data Modeling, and TR on Effective Connectivity Results

As the use of effective connectivity as become more popular, it is important to understand how the results from different analyses compare with each other, as the results from studies employing differing methods for determining connectivity may not reach the same conclusion. Simulated fMRI time series data were used to compare the results from four of the more commonly used computational methods, structural equation modeling, autoregressive analysis, Granger causality, and dynamic causal modeling to determine which may be better suited to the task. The results show that all three methods are able to detect changes in system dynamics. Structural equation modeling appeared to be the least sensitive to changes in TR or source of variance, and Granger causality the most sensitive. The results also suggest that improved reporting on data analyses is necessary, and employing an effect statistic to depict results may remove some of the ambiguity in comparing results across studies using differing methods to determine connectivity.

An intercontinental comparison of the dynamic behavior of mast seeding communities

AbstractCases of mast seeding, involving highly variable seed production synchronized over large geographic areas, provide dramatic examples of resource pulses that have been documented for most major world land masses. Here, we compare the dynamic behavior of two of these systems, with the goal of understanding differences in the long‐term consequences of masting events to their respective communities. Responses to mast events in deciduous oak forests in eastern North America are characteristically complex and of low resilience. That is, each event produces long‐lasting cascading effects in the community, ultimately influencing not only seed consumers such as rodents and deer, but also the parasites and prey of those consumers, diseases transmitted by those parasites, and outbreaks of insect herbivores. In contrast, despite more extreme resource pulsing in New Zealand Nothofagus forests, responses to mast events there are less complex and more resilient, i.e., producing only short‐term (<2 years), smoothly damped, numerical responses by a few species, except after (rare) ‘double‐mast’ events in consecutive years. A detailed examination of the two systems suggests some tentative explanations for the strongly contrasting dynamics of these systems. Firstly, the higher number of species involved in North America seems to reduce resilience by increasing food chain length, lags, and alternative prey, all of which increase the dynamic complexity compared with that in New Zealand. Secondly, lack of a shared evolutionary history among species did not necessarily reduce resilience. Some exotic species showed well‐damped fluctuations (e.g., stoats (Mustela erminea L.) in New Zealand), while other exotics showed complex dynamics (e.g., gypsy moths (Lymantria dispar L.) in North America). Thirdly, recent extinctions of species such as the passenger pigeon (Ectopistes migratorius L.), once the dominant acorn predator in eastern North American forests, have likely produced qualitative changes in system dynamics in both communities. Fourthly, the North American community has more lags and hysteresis, which probably contribute to the greater dynamic complexity in eastern North America than in New Zealand Nothofagus forests. However, positive feedback loops present in North America seem to have little influence on system dynamics. Because the massive perturbations induced by masting events are major recurring challenges to an ecosystem, disentangling the causes of different system responses is likely to lead us to a better understanding of ecosystem function, resilience and stability.

Petri Net Models for Assembly Sequences Planning

Assembly sequences can be represented by a Petri net(PN) which characterizes dynamic system changes and provides a tool for obtaining optimal assembly sequences. In this study some assembly operation constraints are considered in order to obtain more practical sequences which are conformed to real situations. In order to enhancing the efficiency of the assembly sequence planning, knowledge-based Petri net, combining an usual Petri net with expert’s knowledge and experiences, is proposed to construct the assembly model. With the complexity of the product, the product’s assembly model size will be too large to analysis. So the basic subnets are used to reduce the large PN. And the reduced version can be used for the analysis of the original PN. To verify the validity and efficiency of the approach, a variety of assemblies including some complicated products from industry are tested, and the corresponding results are also presented.

Favorable locations for piezo actuators in plates with good control effectiveness andminimal change in system dynamics

Placement and sizing of piezo actuators is normally based on control effectiveness.However, retrofitting of piezoelectric actuators alters the inherent stiffness/mass propertiesof the parent structure. In rotating structures, the additional mass due to piezo patchescontributes to the centrifugal stiffening force. The parent structure is originally designed tohave a certain natural frequency spectrum in relation to the disturbance excitation. In theevent of failure of the active system, the dynamics of the structure with piezos(now rendered passive) will therefore become significant. Thus it will be helpfulto determine locations for mounting piezo patches based on minimal naturalfrequency change yet with good control authority. In this study, a finite element basedprocedure for plate structures is presented. Favorable locations for mountingpiezos based on minimal natural frequency changes are iteratively evolved from aninitial configuration wherein the whole plate is covered with piezos. A modalcontrollability approach has been used for finding piezo mounting locations from a goodcontrollability perspective. The procedure is demonstrated for simply supported square,swept-back, circular and rotating rectangular plates considering the first four modes.

UNDERSTANDING AND PREDICTING EFFECTS OF MODIFIED INTERACTIONS THROUGH A QUALITATIVE ANALYSIS OF COMMUNITY STRUCTURE

Models of ecological communities are traditionally based on relationships between pairs of species, where the strengths of per capita interactions are fixed and independent of population abundance. A growing body of literature, however; describes interactions whose strength is modified by the density of either a third species or by one of the species involved in a pairwise interaction. These modified interactions have been treated as indirect effects, and the terminology addressing them is diverse and overlapping. In this paper we develop a general analytical framework based on a qualitative analysis of community structure to account for the consequence of modified interactions in complex ecological communities. Modified interactions are found to create both direct and indirect effects between species. The sign of a direct effect can change in some instances depending on the magnitude of a key variable or parameter, which leads to a threshold change in system structure and dynamics. By considering alternative structures of a community, we extend our ability to model perturbations that move the system far from a previous equilibrium. Using specific examples, we reinterpret existing results, develop hypotheses to guide experiments or management interventions, and explore the role of modified interactions and positive feedback in creating and maintaining alternative stable states. Through a qualitative analysis of community structure, system feedback is demonstrated as being key in understanding and predicting the dynamics of complex ecological communities.

Fuzzy Logic Modeling and Its Application to A Walking-Beam Reheating Furnace

A fuzzy modeling method is proposed to build the dynamic model of a walking-beam reheating furnace from the recorded data. In the proposed method, the number of membership function on each variable is increased individually and the modeling accuracy is evaluated iteratively. When the modeling accuracy is satisfied, the membership functions on each variable are fixed and the structure of fuzzy model is determined. Because the training data is limited, in this process, as the number of membership function increase, it is highly possible that some rules are missing, i.e., no data in the training set corresponds to the consequent part of a missing rule. To complete the rulebase, the output of the model constructed at the previous step is used to generate the consequent part of the missing rules. Finally, in the real time application, a rolling update scheme to rulebase is introduced to compensate the change of system dynamics and fine tune the rulebase. The proposed method is verified by the application to the modeling of a reheating furnace.

Qualitative effects of inducible defenses in trophic chains

By means of qualitative techniques we analyze the consequences of inducible defenses of species embedded in trophic chains on the community stability and responses of population equilibrium densities to press perturbations. Our results show that the inclusion of inducible defenses in trophic chains leads to profound changes in system dynamics. Inducible defenses increase the likelihood of instability, especially when exhibited by species of lower trophic levels. We obtained biologically reasonable feedback conditions that must be satisfied to ensure stability. Species responses to press perturbation are modified by inducible defenses and their associated costs in multiple ways. Many of the direct effects in the community are reinforced, while indirect effects are either weakened, if they propagate in a top–down direction, or are unaffected if they propagate from basal species. The dominant view of inducible defenses as a stabilizing force seems to be valid only within a biologically constrained parameter space.

Plenary speak: 1: Nonlinear Fault Diagnosis of Dynamical Systems: An Intelligent Control Framework

One of the key goals of intelligent control systems is to achieve high performance with increased reliability, availability, and automation of maintenance procedures. Moreover, the continuous demand for greater productivity has led to more challenging operating conditions for many modern industrial systems. Such conditions increase the possibility of system faults, which are characterized by critical and unpredictable changes in system dynamics. The goal of this presentation is to provide a unified methodology for detecting, isolating and accommodating both abrupt and incipient faults in a class of nonlinear dynamic systems. A detection and approximation estimator is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue is the adaptive residual threshold associated with each isolation estimator. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. Various adaptive approximation techniques and learning algorithms will be presented and illustrated, and directions for future research will be discussed.The technical and social systems of the present day are ever more complex and complicated objects. Their models are characterized by large numbers of state and control variables, time delays, and different time constants. Also they show constraints in their information infrastructure and risk sensitivity aspects. Such systems are called large–scale complex systems (LSS). Hierarchical approach has been for several decades one of the most utilized methodologies for controlling large–scale systems. When human intervention is necessary decision support systems (DSS) can represent a solution. A DSS is an adaptive and evolving information system meant to implement several of the functions of a support team that would otherwise be needed to help the decision-maker to overcome his/her limits and constraints he/she may face when approaching decision problems that count in the organisation. This paper aims at reviewing several aspects concerning LSS control and the utilization and technology of DSS. Particular emphasis is put on real-time DSS and multiparticipant (group) DSS. Several advanced solutions such as mixed knowledge systems, that combine numerical methods with AI-based tools, and the prospects of using Ambient Intelligence concepts in DSS construction are described.

Stability-Based Spindle Design Optimization

Prediction of stable cutting regions is a critical requirement for high-speed milling operations. These predictions are generally made using frequency-response measurements of the tool-holder-spindle set obtained from a nonrotating spindle. However, significant changes in system dynamics occur during high-speed rotation. In this paper, a dynamic high-speed spindle-bearing system model is elaborated on the basis of rotor dynamics prediction and readjusted on the basis of experimental modal identification. The dependency of dynamic behavior on speed range is then investigated and determined with accuracy. Dedicated experiments are carried out in order to confirm model results. They show that dynamic effects due to high rotational speed and elastic deformations, such as gyroscopic coupling and spin softening, have a significant influence on spindle behavior. By integrating the modeled speed-dependent spindle transfer function in the chatter vibration stability approach of Altintas and Budak (1995, CIRPS Ann, 44(1), pp. 357–362), a new dynamic stability lobe diagram is predicted. Significant changes are observed in the stability limits constructed using the proposed approach and allow accurate prediction of cutting conditions to be established. Finally, optimization studies are performed on spindle design parameters in order to obtain a chatter vibration-free cutting operation at the desired speed and depth of cut for a given cutter.

Model-based chatter stability prediction for high-speed spindles

The prediction of stable cutting regions is a critical requirement for high-speed milling operations. These predictions are generally made using frequency response measurements of the tool/holder/spindle set, obtained from a non-rotating spindle. However, significant changes in system dynamics occur during high-speed rotation. In this paper, a dynamic model of a high-speed spindle-bearing system is elaborated on the basis of rotor dynamics predictions, readjusted with respect to experimental modal identification. Variations in dynamic behaviour according to speed range are then investigated and determined with accuracy. Dedicated experiments are carried out in order to confirm model results. By integrating the proposed speed-dependant transfer function into the chatter vibration stability approach of Budak–Altintas [S. Tobias, W. Fishwick, Theory of regenerative machine tool chatter, The Engineer February (1958)] a dynamic stability lobes diagram is predicted. The proposed method enables a new stability lobes diagram to be established that takes into account the effect of spindle speed on dynamic behaviour. Significant variations are observed and allow the accurate prediction of cutting conditions. Finally, experiments are performed in order to validate chatter boundary predictions in practice. The proposed modelling approach can also be used to qualify a spindle design in a given machining process and can easily be extended to other types of spindle.

An adaptive recurrent fuzzy system for nonlinear identification

This paper describes the architecture and training procedure of a recurrent fuzzy system (RFS). The RFS is composed of a fuzzy inference system (FIS) and a delayed feedback connection. The recurrent property comes from feeding the FIS output back to the FIS input via an adjustable feedback parameter. Both the on-line and off-line training procedures based on the backpropagation-through-time (BPTT) algorithm have been investigated. The adjoint model of the RFS is obtained and used to compute the gradients. It is shown that the off-line training is insufficient to adapt to changes in system dynamics. So, an on-line training procedure is derived. In this procedure, a first in first out stack is used to store a certain history of the input-output data to perform a truncated BPTT algorithm. A quasi-Newton optimization method with a line search algorithm is used to adjust the RFS parameters. The performance of the developed RFS is demonstrated by applying to the identification of nonlinear dynamic systems. The simulation studies show that the proposed identification model has the ability to learn dynamics of highly nonlinear systems and compensate system uncertainties. The results are promising for the further application in the area of control and modeling.

Multilevel intelligent scheduling and control system for an automated flow shop manufacturing environment

Current manufacturing scheduling and control systems are incapable of coping with complex system dynamics inherent in real-world situations and, hence, human intervention is required to maintain real-time adaptation and optimization. A unique feature of biological intelligent systems is that they build and improve over their communication, decision-making and control structures in real time autonomously. A challenge is now emerging in the design of manufacturing systems where on-line adaptation and optimization become increasingly important. This paper reports on the development of a new integrated intelligent scheduling and control system for an automated manufacturing environment using a multilevel approach. At the first level, a conventional scheduling and control system is considered, then at the second level, a new fuzzy logic mechanism is developed to enable the conventional system to improve and perceive the changes of system parameters adaptively. A new perturbation mechanism is embedded in the third level to implement on-line optimization for coping with the more complex structural changes of system dynamics. The final level is composed of artificial neural networks that can learn from experiences provided by the perturbation mechanism. The approach is designed to improve system intelligence gradually to cope with various forms of systems dynamics. A fully automated flow shop manufacturing system is taken to demonstrate this approach.

Attitude Determination from GNSS Using Adaptive Kalman Filtering

An adaptive Kalman filtering approach is proposed for attitude determination to replace the fixed (conventional) Kalman filtering approach. The filter is used to adaptively reflect system dynamics changes or rapid changes in vehicle trajectory. The estimation procedure is carried out through the use of a measurement residual sequence. The sequence is used as a piece-wise stationary process inside an estimation window. The measurement noise covariance matrix and the system noise matrix are adaptively estimated. An extended Kalman filter approach with iteration of the states within-an-epoch was performed to overcome the non-linearity of the observation equations. A test was performed to evaluate the proposed technique. Different trajectory scenarios are presented to show the difference in performance between the adaptive Kalman filter and the conventional one. Results show that the proposed adoptive filtering approach has a better performance than the conventional filter.

Analysis and synthesis of reliable flight control systems via parameter-dependent Lyapunov functions

A reliable tracking control scheme is developed to tolerate faults which result in changes in system dynamics, such as wing/control surface impairment faults and actuator-stuck faults of aircraft. New analysis and synthesis methods are developed to reduce conservativeness of existing methods by introducing parameter-dependent Lyapunov functions and slack variables. The design method based on multi-model and multi-objective optimization guarantees closed-loop system stability and optimized tracking performance of the system in normal operation, while maintaining a certain level of tracking performance when faults occur. A case study of actuator-stuck faults for a fighter aircraft verifies the effectiveness of the method.

Analysis of electrochemical parameters in time domain during the passive layer cracking occurring on the 304L stainless steel in chlorides solution under tensile stresses

Dynamic electrochemical impedance spectroscopy (DEIS) has been applied for detailed analysis of the passive–active transient region during the passive layer cracking process. The effect of applied potential and tensile stresses on the passive layer rupture of type 304L stainless steel (SS) immersed in 0.5 M NaCl solution at room temperature was examined. This paper presents instantaneous impedance spectra obtained for 304L stainless steel samples at the different potential values that refer to the transition from passive into active state while crack of the passive layer took place. Besides, differential dependencies of electrochemical parameters versus relative elongation have been presented to illustrate the system's dynamics changes.