Internal Feedback Loop Research Articles

Active regulation of receptor ratios controls integration of quorum‐sensing signals in Vibrio harveyi

Quorum sensing is a chemical signaling mechanism used by bacteria to communicate and orchestrate group behaviors. Multiple feedback loops exist in the quorum-sensing circuit of the model bacterium Vibrio harveyi. Using fluorescence microscopy of individual cells, we assayed the activity of the quorum-sensing circuit, with a focus on defining the functions of the feedback loops. We quantitatively investigated the signaling input-output relation both in cells with all feedback loops present as well as in mutants with specific feedback loops disrupted. We found that one of the feedback loops regulates receptor ratios to control the integration of multiple signals. Together, the feedback loops affect the input-output dynamic range of signal transmission and the noise in the output. We conclude that V. harveyi employs multiple feedback loops to simultaneously control quorum-sensing signal integration and to ensure signal transmission fidelity.

Experimental Evaluation of a Two Degree of Freedom Capacitive MEMS Sensor For Velocity Measurements

This paper presents the experimental tests carried out on a novel two-degrees-of-freedom capacitive MEMS sensor. The sensor comprises an internal feedback loop, which produces a sky-hook damping effect on the principal seismic mass of the transducer so that the output of the sensor is proportional to the base velocity over a low audio frequency range. The four frequency response functions that characterise the dynamic response of the MEMS block have been measured and used to simulate the closed loop response of the transducer. The off–line test shows that, at low audio frequencies between about 100Hz and 1kHz, the spectrum of the sensor output signal is proportional to the base velocity. Moreover at frequencies above the fundamental resonance frequency of the transducer, the output signal shows a -90o phase lag with reference to the base velocity. These two properties are of great interest for the implementation of vibration control systems using feedback loops with collocated sensor and actuator transducers.

PTEN Tumor Suppressor Network in PI3K-Akt Pathway Control

The PI3K-Akt pathway is a major survival pathway activated in cancer. Efforts to develop targeted therapies have not been fully successful, mainly because of extensive internal intrapathway or external interpathway negative feedback loops or because of networking between pathway suppressors. The PTEN tumor suppressor is the major brake of the pathway and a common target for inactivation in somatic cancers. This review will highlight the networking of PTEN with other inhibitors of the pathway, relevant to cancer progression. PTEN constitutes the main node of the inhibitory network, and a series of convergences at different levels in the PI3K-Akt pathway, starting from those with growth factor receptors, will be described. As PTEN exerts enzymatic activity as a phosphatidylinositol-3,4,5-trisphosphate (PIP(3)) phosphatase, thus opposing the activity of PI3K, the concerted actions to increase the availability of PIP(3) in cancer cells, relying either on other phosphoinositide enzymes or on the intrinsic regulation of PTEN activity by other molecules, will be discussed. In particular, the synergy between PTEN and the circle of its direct interacting proteins will be brought forth in an attempt to understand both the activation of the PI3K-Akt pathway and the connections with other parallel oncogenic pathways. The understanding of the interplay between the modulators of the PI3K-Akt pathway in cancer should eventually lead to the design of therapeutic approaches with increased efficacy in the clinic.

Integrin-mediated cell attachment induces a PAK4-dependent feedback loop regulating cell adhesion through modified integrin alpha v beta 5 clustering and turnover.

Cell-to-extracellular matrix adhesion is regulated by a multitude of pathways initiated distally to the core cell-matrix adhesion machinery, such as via growth factor signaling. In contrast to these extrinsically sourced pathways, we now identify a regulatory pathway that is intrinsic to the core adhesion machinery, providing an internal regulatory feedback loop to fine tune adhesion levels. This autoinhibitory negative feedback loop is initiated by cell adhesion to vitronectin, leading to PAK4 activation, which in turn limits total cell-vitronectin adhesion strength. Specifically, we show that PAK4 is activated by cell attachment to vitronectin as mediated by PAK4 binding partner integrin αvβ5, and that active PAK4 induces accelerated integrin αvβ5 turnover within adhesion complexes. Accelerated integrin turnover is associated with additional PAK4-mediated effects, including inhibited integrin αvβ5 clustering, reduced integrin to F-actin connectivity and perturbed adhesion complex maturation. These specific outcomes are ultimately associated with reduced cell adhesion strength and increased cell motility. We thus demonstrate a novel mechanism deployed by cells to tune cell adhesion levels through the autoinhibitory regulation of integrin adhesion.

Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network

This study focuses on the design of robust path tracking control for a mobile robot via a dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.

STABILISATOR SISTEM TENAGA BERBASIS JARINGAN SYARAF TIRUAN BERULANG UNTUK SISTEM MESIN TUNGGAL

In this paper, recurrent neural network (RNN) is used to design power system stabilizer (PSS) due to its advantage on the dependence not only on present input but also on past condition. A RNN-PSS is able to capture the dynamic response of a system without any delays caused by external feedback, primarily bythe internal feedback loop in recurrent neuron. In this paper, RNNPSS consists of a RNN-identifier and a RNN-controller. The RNN-Identifier functions as the tracker of dynamics characteristics of the plant, while the RNN-controller is used to damp the system’s low frequency oscillations. Simulation results using MATLAB demonstrate that the RNNPSS can successfully damp out oscillation and improve the performance of the system.

A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

BackgroundSeveral authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).Methodology/Principal FindingsThis study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements.Conclusions/SignificanceThis is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field.

Robust Implementation of Controllers Containing Distributed-Delay Elements: A Case-Study

This paper studies the implementation of controllers containing distributed-delay internal feedback elements (e.g., dead-time compensators) using a laboratory DC motor. It is demonstrated that the verbatim use of approximation methods available in the literature may not be feasible for the implementation even on relatively powerful real-time control hardware. We argue that the main reason is the lack of robustness of the internal feedback loop of the controller. Some remedies, allowing to make this loop more robust, are proposed and experimentally validated.

Seismic Velocity Sensor With an Internal Sky-Hook Damping Feedback Loop

This paper presents a new seismic velocity sensor with an internal feedback control loop. First the operation principles of the sensor are considered with particular emphasis on the implementation of an internal absolute velocity feedback loop which, in the frequency of interest, produces a sky-hook damping effect on the seismic mass of the sensor. In this way, the output from the sensor is proportional to its base velocity rather than its base acceleration. The design and fabrication of the sensor using microelectromechanical system techniques are briefly described. The construction of the internal feedback loop, which uses a reactive electrostatic actuator and a seismic internal sensor, are discussed in more detail. Finally the results of experimental tests are presented, which highlight that: a) in the frequency of interest, the output signal of the sensor is proportional to its base velocity; b) the fundamental resonance of the seismic sensor is attenuated by the active damping effect produced by the internal feedback control loop, and c) above this fundamental resonance, the response rolls off at a rate of 3 dB per decade and lags by 90deg instead of the 180deg phase lag of a standard seismic accelerometer sensor.

Accounting for time-dependent covariates in driving simulator studies

Driving involves multiple cognitive processes that are influenced by a dynamic external environment and internal feedback loops. These processes are typically studied in a simulator environment to capture time-dependent driver performance measures. The primary objective of this research is to show that data analysis techniques that ignore or improperly treat time-dependent covariates will lead to erroneous estimates and conclusions. This is demonstrated with a driving simulator study that was used to test whether a significant decrease in performance occurs in the presence of auditory and visual distractions. A total of 28 drivers participated in a 2 (age) × 7 (strategy) repeated measures experiment. The response variable—accelerator release time—was analysed with and without consideration of time-dependent covariates. Using the inverse headway distance as a time-dependent covariate corrected logically inconsistent results obtained when the covariate was ignored. This indicates that ignoring covariates can actually lead to inappropriate design or policy implications.

La main vers la cible : intégration multi-sensorielle et contrôle en ligne du mouvement de pointage

The central nervous system, the interface between sensory and motor systems, allows us to interact with our environment. This review presents a critical analysis of the current knowledge on the neuropsychological processes underlying the online control of goal-directed arm movements toward visual targets. While we live within an uncertain environment, the visual and proprioceptive systems are continuously used to update body and space representations, in order to control our movements. However, it is still not well understood how hand position is determined : is the hand localization process based on visual, proprioceptive, efferent signals ? It has been shown that visual, proprioceptive and internal feedback loops can be used to control in flight fast reaching movements. Recent results support the idea that peripheral and central feedback signals are integrated in an optimal fashion to control the movements. This review highlights the rapidity of information processing and discusses the specificity of the online motor control as a function of the task constraints, before suggesting new lines of research.

Responses of Collicular Fixation Neurons to Gaze Shift Perturbations in Head-Unrestrained Monkey Reveal Gaze Feedback Control

A prominent hypothesis in motor control is that endpoint errors are minimized because motor commands are updated in real time via internal feedback loops. We investigated in monkey whether orienting saccadic gaze shifts made in the dark with coordinated eye-head movements are controlled by feedback. We recorded from superior colliculus fixation neurons (SCFNs) that fired tonically during fixation and were silent during gaze shifts. When we briefly (<or=700 ms) interrupted gaze shifts by transiently braking head movements, SCFNs fired steadily during brake-induced gaze immobility, and their mean frequency was inversely related to the remaining distance between current gaze position and the target. After head release, a corrective gaze saccade brought gaze on the unseen goal, and SCFN firing frequency peaked. The results support gaze feedback control and show that the SC is part of a network that encodes, during orientation, the distance between eye and target, irrespective of gaze trajectory characteristics.

Opportunities and challenges created by terrorism

Besides being pushed by technical development and pulled by the demand for hi-tech products, technology is also often influenced greatly by the sudden impacts from other external environments. As shown by the aftermath of the 9/11 attacks (2001), Bali bombings (2002, 2005), Madrid bombings (2004), and London bombings (2005), some technologies have been fundamentally affected by terrorism, especially those concerning globalization, infrastructure, corporations, education, and individuals. Using system dynamics (SD) methodology, our paper first examines the causes of terrorism and why the United States was chosen as the target for the 9/11 terror attacks. The concept of SD helps analysts realize the variation of a complicated system and perceive how an internal feedback loop within a system impacts the whole system's behavior. After suffering the 9/11 attacks, the American-led coalition carefully considered how to study and develop effective methods for anti-terrorism strategies. These anti-terrorism efforts will have a major impact on technology development, and many opportunities and challenges are likely to arise from such development. Based on the qualitative analytic approach of causal loops, this article explores in detail the opportunities and challenges for technology development prompted by terrorism. The contribution of our study lies in appropriately analyzing links between terrorism and technology development in order to explain the present relevant technology situation and to initiate a discussion of future technology development trends.

反射とその適応的な抑制による腱駆動型ロボットの軌道追従制御

In this paper, a trajectory tracking control method for the tendon-driven robot is presented. In the human motor control system, it is known that the spinal reflex and the cerebellum play a key role to achieve the desired limb motions and have some characteristic structures different from the conventional robot control systems, such as the internal positive feedback loops. From the viewpoint of control, the positive feedback is inconvenient since it might potentially destabilize the limb motion. However, in the biological system, the positive feedback is actively used rather than avoided and has significant role to achieve the natural human limb motions. Thus, in this paper, we attempted to establish a trajectory tracking controller for the tendon-driven robots with reference to the biological control system composed of the spinal cord and cerebellum. The stability of the controlled system was proven along the Popov's hyper-stability theory. The effectiveness of the proposed controller was demonstrated through some computer simulations and experiments using a 2 link tendon-driven robot.

DDV를 이용한 압력 제어시스템의 강인제어

In this paper, it is proposed that the method for constituting pressure control system controlled by Direct Drive Valve (DDV). The DDV has a pressure-feedback-loop itself. It can eliminate non-linearity and uncertainty oi hydraulic system such as uncertain discharge coefficient and change of bulk-modulus. However, the internal feedback-loop can not compensate them perfectly. And fixed gain of the DDV's internal feedback-loop is not proper to apply it through wide pressure range. The steady state error and nonlinear characteristic of transient behaviour is observed in the experiment. So another controller is needed for the desirable performance of the system. To compose the controller, the pressure control system controlled by DDV is modeled mathematically and the parameters of the model are identified using signal-compression method. Then sliding mode controller is designed based on mathematical model. Desirable performance of the pressure control system controlled by DDV is obtained.

Velocity sensor with an internal feedback control loop

This paper presents a new velocity sensor whose output is directly proportional to velocityat low frequencies, but has a well damped resonance after which it has a controlled roll-off.It is designed to be used in a feedback loop with a closely located piezoelectric patchactuator to form a sensor–actuator pair for the implementation of active damping.The velocity sensor consists of a principal spring–mass seismic sensor with aninternal direct velocity feedback control loop. This internal feedback loop uses aseparate control spring–mass seismic sensor and a reactive actuator, which are fixedon the seismic mass of the principal sensor. The control gain is tuned to obtaintwo effects: first, the output signal from the principal sensor becomes directlyproportional to the velocity at the base of the sensor itself and second, the fundamentalresonance peak is reduced by the active damping effect of the internal feedback loop.The practical feasibility is then studied by considering a prototype model. Thestability of the internal feedback control loop has been assessed first, followingwhich the frequency response function of the sensor with and without the internalfeedback loop has been measured. The experimental measurements have shown thatthe internal feedback loop is conditionally stable but guarantees sufficient gainmargins to obtain the desired velocity output from the sensor. The sensor hasbeen successfully tested with a closed loop, and shows the desired velocity outputwith no resonance at the fundamental natural frequency of the seismic sensor.

Remapping Auditory-Motor Representations in Voice Production

Evidence regarding visually guided limb movements suggests that the motor system learns and maintains neural maps between motor commands and sensory feedback. Such systems are hypothesized to be used in a feed-forward control strategy that permits precision and stability without the delays of direct feedback control. Human vocalizations involve precise control over vocal and respiratory muscles. However, little is known about the sensorimotor representations underlying speech production. Here, we manipulated the heard fundamental frequency of the voice during speech to demonstrate learning of auditory-motor maps. Mandarin speakers repeatedly produced words with specific pitch patterns (tone categories). On each successive utterance, the frequency of their auditory feedback was increased by 1/100 of a semitone until they heard their feedback one full semitone above their true pitch. Subjects automatically compensated for these changes by lowering their vocal pitch. When feedback was unexpectedly returned to normal, speakers significantly increased the pitch of their productions beyond their initial baseline frequency. This adaptation was found to generalize to the production of another tone category. However, results indicate that a more robust adaptation was produced for the tone that was spoken during feedback alteration. The immediate aftereffects suggest a global remapping of the auditory-motor relationship after an extremely brief training period. However, this learning does not represent a complete transformation of the mapping; rather, it is in part target dependent.

Theory of backward distributed-feedback optical parametric amplifiers and oscillators

We derive a coupled-wave theory for backward phase-matched optical parametric amplifiers and oscillators in a dielectric modulated nonlinear optical material. Unlike a forward phase-matched distributed-feedback optical parametric oscillator, a backward oscillator of this type can have high-threshold gap modes at Bragg resonance and low-threshold detuned modes. In addition to the structure feedback loop, the internal feedback loop in the counterpropagating waves strongly influences the resonant-mode formation in this oscillator. A backward distributed-feedback optical parametric oscillator also exhibits the mode-dependent oscillation threshold similar to that for a distributed-feedback laser and yet has the additional advantage of wavelength selectivity in a much broader parametric bandwidth.

Digital SQUID Sensor Based on SFQ Technique

For high sensitive measurements of small magnetic fields a Digital SQUID is superior to conventional analog SQUIDs in terms of dynamic properties, but recent realizations as single-flux-quantum (SFQ) circuit suffers from high complexity. A new kind of Digital SQUID as a full digital sensor device with the advantage of a small number of Josephson junctions and a large slew rate was developed. The circuit consists of basic SFQ cells and an internal digital feedback loop. The operation with a bidirectional clock signal ensures a decreased effort on superconducting electronics. The SFQ/dc converter and an additional voltage driver provides a processable digital output signal for hybrid systems including semiconductor electronics. The sensor circuit was simulated, optimized and fabricated in niobium technology. From investigation of dynamic properties of the circuit we expect a flux slew rate in the gigahertz range.