Reproducing Five Motor Behaviors in a Salamander Robot With Virtual Muscles and a Distributed CPG Controller Regulated by Drive Signals and Proprioceptive Feedback

Diverse locomotor behaviors emerge from the interactions between the spinal central pattern generator (CPG), descending brain signals and sensory feedback. Salamander motor behaviors include swimming, struggling, forward underwater stepping, and forward and backward terrestrial stepping. Electromyographic and kinematic recordings of the trunk show that each of these five behaviors is characterized by specific patterns of muscle activation and body curvature. Electrophysiological recordings in isolated spinal cords show even more diverse patterns of activity. Using numerical modeling and robotics, we explored the mechanisms through which descending brain signals and proprioceptive feedback could take advantage of the flexibility of the spinal CPG to generate different motor patterns. Adapting a previous CPG model based on abstract oscillators, we propose a model that reproduces the features of spinal cord recordings: the diversity of motor patterns, the correlation between phase lags and cycle frequencies, and the spontaneous switches between slow and fast rhythms. The five salamander behaviors were reproduced by connecting the CPG model to a mechanical simulation of the salamander with virtual muscles and local proprioceptive feedback. The main results were validated on a robot. A distributed controller was used to obtain the fast control loops necessary for implementing the virtual muscles. The distributed control is demonstrated in an experiment where the robot splits into multiple functional parts. The five salamander behaviors were emulated by regulating the CPG with two descending drives. Reproducing the kinematics of backward stepping and struggling however required stronger muscle contractions. The passive oscillations observed in the salamander's tail during forward underwater stepping could be reproduced using a third descending drive of zero to the tail oscillators. This reduced the drag on the body in our hydrodynamic simulation. We explored the effect of local proprioceptive feedback during swimming and forward terrestrial stepping. We found that feedback could replace or reduce the need for different drives in both cases. It also reduced the variability of intersegmental phase lags toward values appropriate for locomotion. Our work suggests that different motor behaviors do not require different CPG circuits: a single circuit can produce various behaviors when modulated by descending drive and sensory feedback.

Multi-Input-Multi-Output Continuous Swept-Sine Vibration Test Realization by Inverse Multistep Prediction Model

As frequency-varying sine excitations in rotating machines are always emerging during run-ups and shutdowns, the multi-input-multi-output (MIMO) swept-sine test is of utter significance in product validation. At present, swept-sine vibration tests are mostly conducted with frequency-domain methods, where drive spectra are generated and updated by frequency response function (FRF), and drive signals are then generated with sinusoid oscillators. In this paper, a time-domain approach using an inverse system method based on a multistep prediction model is developed to realize the MIMO continuous swept-sine vibration test. First, the multistep prediction model of the original system is estimated in the time domain. Then, the inverse multistep prediction model is derived. After that, this model is truncated to guarantee the robustness of the inverse system and the smoothness of the generated drive signals. At last, the proposed method is validated by a simulation example with a cantilever beam and an actual test by using a three-axis shaker. The results show that the MIMO continuous swept-sine vibration test can be operated effectively by the proposed method.

Open-Circuit Fault-Tolerant Design of the Cascaded H-Bridge Rectifier Incorporating Reactive Power Compensation

This paper proposes an open-circuit fault-tolerant design for the cascaded H-Bridge rectifier incorporating reactive power compensation. If one or two switching devices of the H-bridge modules are fault, the drive signals of the faulty H-bridge modules will be artificially redistributed into the bridgeless mode (including the boost bridgeless mode, the symmetric boost bridgeless mode, the totem-pole bridgeless mode and the symmetry totem-pole bridgeless mode) and cooperate with the normally operated H-bridge modules. In this case, the faulty cascaded H-bridge rectifier is not only able to achieve active power transmission, but also can still provide part of reactive power compensation when injecting reactive power from the power grid. Nonetheless, the reactive power that it can supply will be limited, due to the unidirectional characteristics of the bridgeless mode for the faulty modules. Therefore, a method for calculating its adjustable power factor angle range is also presented, which provides the basis for the faulty modules switching to the bridgeless mode. Then, a control strategy of the cascaded H-bridge rectifier incorporating reactive power compensation under the faulty condition and normal operation is presented. Finally, an experimental platform with a single-phase cascaded H-bridge rectifier containing three cells is given to verify the proposed theories.

Reconstruction of Process Forces in a Five-Axis Milling Center with a LSTM Neural Network in Comparison to a Model-Based Approach

Based on the drive signals of a milling center, process forces can be reconstructed. Therefore, a novel approach is presented to reconstruct the process forces with a long short-term memory neural network (LSTM) using drive signals as an input. The LSTM is evaluated and compared to a model-based approach. The latter compensates nonlinearities and disturbances such as friction and inertia. For training of the LSTM, multiple milling processes are considered to enhance the generalizability. Training data is generated by recording drive signals and process forces measured by a dynamometer. The LSTM is then evaluated using a test set, which comprises new process parameters. It is shown that the LSTM has a lower root mean square error (RMSE) in comparison to the model-based approach. Especially, when changing the feed motion direction during milling, the neural network clearly outperforms the model-based approach. Nevertheless, there are processes, where the LSTM induced oscillations, which do not correspond to the measured forces.

Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine

Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves).

An Analysis Method for Capacitive Micromachined Ultrasound Transducer (CMUT) Energy Conversion during Large Signal Operation

With Capacitive Micromachined Ultrasound Transducers (CMUTs) increasingly being used for high intensity, large signal ultrasound applications and several drive methods being proposed, the efficiency of these devices in this operation regime have not been quantitatively evaluated. Since well-known frequency and capacitance-based coupling coefficients definitions are not valid for large signal, nonlinear operation, an energy-based definition should be used. In this paper, an expression for mechanical energy in a CMUT is obtained based on the assumption that CMUT is a linear time varying capacitor in all regimes of operation. This expression is evaluated by the help of an experimentally verified nonlinear CMUT model to define an energy conversion ratio (ECR) which can be considered as a coupling coefficient valid for all regimes of operation. This parameter is validated in the small signal regime and then used to evaluate CMUT performance with various large drive signals. The quantitative modeling results show that CMUTs do not need DC bias to achieve high efficiency large signal transduction: AC only signals at half the operation frequency with amplitudes beyond the collapse voltage can provide efficiencies (ECR) above 0.9 with harmonic content below −25 dB. Based on these results, ECR variation with membrane geometry and parasitic capacitance are given as examples for device optimization. The overall modeling approach is also qualitatively validated by experiments.

A Modified One-Cycle-Control Method for Modular Multilevel Converters

In this paper, a new One-Cycle-Control (OCC) method is designed for a modular multilevel converter (MMC) based on the principle of the equivalent resistance constant. The proposed controller has a simple structure and a small amount of calculation by cancelling the current inner loop proportional integral (PI) controller and the inverse transform in the traditional direct-quadrature (DQ) control. Compared to the traditional OCC controller, the new one separates the control method from the modulation strategy, making it possible to use not only carrier-based pulse-width modulation (PWM), but also nearest level modulation PWM to generate drive signals. Besides, the independent control of the active and the reactive power is implemented by injecting a reference current with the same phase of the supply voltage or a reference current which lags the supply voltage by π/2 into the controller, so the converter can operate in four quadrants and it can work in either a grid-connect or off-grid environment. The feasibility and the performance of the proposed OCC method have been validated by both the simulation under the MATLAB/SIMULINK (R2012a) environment and experimental results.

Rare-Variant Studies to Complement Genome-Wide Association Studies

Genome-wide association studies (GWASs) have revolutionized human disease genetics by discovering tens of thousands of associations between common variants and complex diseases. In parallel, huge technological advances in DNA sequencing have made it possible to measure and analyze rare variation in populations. This review considers these two stories and how they have come together. We first review the history of GWASs and sequencing. We then consider how to understand the biological mechanisms that drive signals of strong association in the absence of rare-variant studies. We describe how rare-variant studies complement these approaches and highlight both data generation and statistical challenges in their interpretation. Finally, we consider how certain special study designs, such as those for families and isolated populations, fit in this paradigm.

Cellular Origin of [18F]FDG-PET Imaging Signals During Ceftriaxone-Stimulated Glutamate Uptake: Astrocytes and Neurons

Ceftriaxone stimulates astrocytic uptake of the excitatory neurotransmitter glutamate, and it is used to treat glutamatergic excitotoxicity that becomes manifest during many brain diseases. Ceftriaxone-stimulated glutamate transport was reported to drive signals underlying [18F]fluorodeoxyglucose-positron emission tomographic ([18F]FDG-PET) metabolic images of brain glucose utilization and interpreted as supportive of the notion of lactate shuttling from astrocytes to neurons. This study draws attention to critical roles of astrocytes in the energetics and imaging of brain activity, but the results are provocative because (1) the method does not have cellular resolution or provide information about downstream pathways of glucose metabolism, (2) neuronal and astrocytic [18F]FDG uptake were not separately measured, and (3) strong evidence against lactate shuttling was not discussed. Evaluation of potential metabolic responses to ceftriaxone suggests lack of astrocytic specificity and significant contributions by pre- and postsynaptic neuronal compartments. Indeed, astrocytic glycolysis may not make a strong contribution to the [18F]FDG-PET signal because partial or complete oxidation of one glutamate molecule on its uptake generates enough ATP to fuel uptake of 3 to 10 more glutamate molecules, diminishing reliance on glycolysis. The influence of ceftriaxone on energetics of glutamate-glutamine cycling must be determined in astrocytes and neurons to elucidate its roles in excitotoxicity treatment.