Shape memory alloy-based small crawling robots inspired by C. elegans

Inspired by its simple musculature, actuation and motion mechanisms, we have developed a small crawling robot that closely mimics the model organism of our choice: Caenorhabditis elegans. A thermal shape memory alloy (SMA) was selected as an actuator due to the similarities of its properties to C. elegans muscles. Based on the anatomy of C. elegans, a 12-unit robot was designed to generate a sinusoidal undulating motion. Each body unit consisting of a pair of SMA actuators is serially connected by rigid links with an embedded motion control circuit. A simple binary operation-based motion control mechanism was implemented using a microcontroller. The assembled robot can execute C. elegans-like motion with a 0.17 Hz undulation frequency. Its motion is comparable to that of a real worm.     

Learning combined feedback and feedforward control of a musculoskeletal system

The goal of this paper is the learning of neuromuscular control, given the following necessary conditions: (1) time delays in the control loop, (2) non-linear muscle characteristics, (3) learning of feedforward and feedback control, (4) possibility of feedback gain modulation during a task. A control system and learning methodology that satisfy those conditions is given. The control system contains a neural network, comprising both feedforward and feedback control. The learning method is backpropagation through time with an explicit sensitivity model. Results will be given for a one degree of freedom arm with two muscles. Good control results are achieved which compare well with experimental data. Analysis of the controller shows that significant differences in controller characteristics are found if the loop delays are neglected. During a control task the system shows feedback gain modulation, similar to experimentally found reflex gain modulation during rapid voluntary contraction. If only limited feedback information is available to the controller the system learns to co-contract the antagonistic muscle pair. In this way joint stiffness increases and stable control is more easily maintained. Received: 7 November 1995 / Accepted in revised form: 13 February 1996     

Text-independent speaker verification using Minimal Resource Allocation Networks

This paper presents a text-independent speaker verification system based on an online Radial Basis Function (RBF) network referred to as Minimal Resource Allocation Network (MRAN). MRAN is a sequential learning RBF, in which hidden neurons are added or removed as training progresses. LP-derived cepstral coefficients are used as feature vectors during training and verification phases. The performance of MRAN is compared with other well-known RBF and Elliptical Basis Function (EBF) based speaker verification methods in terms of error rates and computational complexity on a series of speaker verification experiments. The experiments use data from 258 speakers from the phonetically balancedcontinuous speech corpus TIMIT. The results show that MRAN produces comparable error rates to other methods with much less computational complexity.     

Modeling and Design of H-Infinity Controller for Piezoelectric Actuator LIPCA

<正> We proposed a dynamic model identification and design of an H-Infinity (i.e.H_∞) controller using a LightweightPiezo-Composite Actuator (LIPCA).A second-order dynamic model was obtained by using input and output data, and applyingan identification algorithm.The identified model coincides well with the real LIPCA.To reduce the resonating mode that istypical of piezoelectric actuators, a notch filter was used.A feedback controller using the H_∞ control scheme was designed basedon the identified dynamic model; thus, the LIPCA can be easily used as an actuator for biomemetic applications such as artificialmuscles or macro/micro positioning in bioengineering.The control algorithm was implemented using a microprocessor, analogfilters, and power amplifying drivers.Our simulation and experimental results demonstrate that the proposed control algorithmworks well in real environment, providing robust performance and stability with uncertain disturbances.     

Flexible Kernel Memory

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.     

Biomimetic Construction of Category Mental Imagery Based on Recognition Mechanism of Visual Cortex of Human Brain

The principle of homology-continuity in Multi-Dimensional Biomimetic Informatics Space is applied to construct the identifying mechanism of category of deep representation of mental imagery. The model of each cerebral region involved in recognizing is established respectively and a feedforward method for establishing category mental imagery is proposed. First, the model of feature acquisition is developed based on Hubel-Wiesel model, and Gaussian function is used to simulate the simple cell receptive field to satisfy the specific function of visual cortex. Second, multiple input aggregation operation is employed to simulate the feature output of complex cells to get the invariance representation in feature space. Then, imagery basis is extracted by unsupervised learning algorithm based on the primary feature and category mental imagery is obtained by building Radial Basis Function (RBF) network. Finally, the system model is tested by training set and test set composed of real images. Experimental results show that the proposed method can establish valid deep representation of these samples, based on which the biomimetic construction of category mental imagery can be achieved. This method provides a new idea for solving imagery problem and studying imagery thinking.     

Networks and the best approximation property

Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well continuous functions (Cybenko 1988, 1989; Funahashi 1989; Stinchcombe and White 1989). We prove that networks derived from regularization theory and including Radial Basis Functions (Poggio and Girosi 1989), have a similar property. From the point of view of approximation theory, however, the property of approximating continuous functions arbitrarily well is not sufficient for characterizing good approximation schemes. More critical is the property ofbest approximation. The main result of this paper is that multilayer perceptron networks, of the type used in backpropagation, do not have the best approximation property. For regularization networks (in particular Radial Basis Function networks) we prove existence and uniqueness of best approximation.     

EEG source localization: comparative study of classical and neural network methods.

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg-Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.     

Fuzzy regression modeling for tool performance prediction and degradation detection

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.     

Advanced control of dissolved oxygen concentration in fed batch cultures during recombinant protein production

Design and experimental validation of advanced pO₂ controllers for fermentation processes operated in the fed-batch mode are described. In most situations, the presented controllers are able to keep the pO₂ in fermentations for recombinant protein productions exactly on the desired value. The controllers are based on the gain-scheduling approach to parameter-adaptive proportional-integral controllers. In order to cope with the most often appearing distortions, the basic gain-scheduling feedback controller was complemented with a feedforward control component. This feedforward/feedback controller significantly improved pO₂ control. By means of numerical simulations, the controller behavior was tested and its parameters were determined. Validation runs were performed with three Escherichia coli strains producing different recombinant proteins. It is finally shown that the new controller leads to significant improvements in the signal-to-noise ratio of other key process variables and, thus, to a higher process quality.     

A radial basis function network approach for the computation of inverse continuous time variant functions

This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.     

Stability and Control of Human Trunk Movement During Walking

A mathematical model has been developed to study the control mechanisms of human trunk movement during walking. The trunk is modeled as a base-excited inverted pendulum with two-degrees of rotational freedom. The base point, corresponding to the bony landmark of the sacrum, can move in three-dimensional space in a general way. Since the stability of upright posture is essential for human walking, a controller has been designed such that the stability of the pendulum about the upright position is guaranteed. The control laws are developed based on Lyapunov' stability theory and include feedforward and linear feedback components. It is found that the feedforward component plays a critical role in keeping postural stability, and the linear feedback component, (resulting from viscoelastic function of the musculoskeletal system) can effectively duplicate the pattern of trunk movement. The mathematical model is validated by comparing the simulation results with those based on gait measurements performed in the Biomechanics Laboratory at the University of Manitoba.     

Stability and Control of Human Trunk Movement During Walking

A mathematical model has been developed to study the control mechanisms of human trunk movement during walking. The trunk is modeled as a base-excited inverted pendulum with two-degrees of rotational freedom. The base point, corresponding to the bony landmark of the sacrum, can move in three-dimensional space in a general way. Since the stability of upright posture is essential for human walking, a controller has been designed such that the stability of the pendulum about the upright position is guaranteed. The control laws are developed based on Lyapunov's stability theory and include feedforward and linear feedback components. It is found that the feedforward component plays a critical role in keeping postural stability, and the linear feedback component, (resulting from viscoelastic function of the musculoskeletal system) can effectively duplicate the pattern of trunk movement. The mathematical model is validated by comparing the simulation results with those based on gait measurements performed in the Biomechanics Laboratory at the University of Manitoba.     

High Frequency Thermal Energy Harvesting Using Magnetic Shape Memory Films

A new method for thermal energy harvesting at small temperature difference and high cycling frequency is presented that exploits the unique magnetic properties and actuation capability of magnetic shape memory alloy (MSMA) films. Polycrystalline films of the Ni_50.4Co_3.7Mn_32.8In_13.1 alloy are tailored, showing a large abrupt change of magnetization and low thermal hysteresis well above room temperature. Based on this material, a free‐standing film device is designed that exhibits thermomagnetically induced actuation between a heat source and sink with short heat transfer times. The cycling frequency of the device is tuned by mechanical frequency up‐conversion to over 200 Hz. An integrated pick‐up coil converts the thermally induced change of magnetization as well as the kinetic energy to electricity. For a temperature change of 10 K, the maximum peak power density is in the order of 5 mW cm^‐3.     

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.     

Comparison of statistical methods for identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from randomly amplified polymorphic DNA patterns

Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25-36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.     

The study of neural network-based controller for controlling dissolved oxygen concentration in a sequencing batch reactor

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.     

An artificial muscle actuator for biomimetic underwater propulsors

In this paper, we introduce the analytical framework of the modeling dynamic characteristics of a soft artificial muscle actuator for aquatic propulsor applications. The artificial muscle used for this underwater application is an ionic polymer-metal composite (IPMC) which can generate bending motion in aquatic environments. The inputs of the model are the voltages applied to multiple IPMCs, and the output can be either the shape of the actuators or the thrust force generated from the interaction between dynamic actuator motions and surrounding water. In order to determine the relationship between the input voltages and the bending moments, the simplified RC model is used, and the mechanical beam theory is used for the bending motion of IPMC actuators. Also, the hydrodynamic forces exerted on an actuator as it moves relative to the surrounding medium or water are added to the equations of motion to study the effect of actuator bending on the thrust force generation. The proposed method can be used for modeling the general bending type artificial muscle actuator in a single or segmented form operating in the water. The segmented design has more flexibility in controlling the shape of the actuator when compared with the single form, especially in generating undulatory waves. Considering an inherent nature of large deformations in the IPMC actuator, a large deflection beam model has been developed and integrated with the electrical RC model and hydrodynamic forces to develop the state space model of the actuator system. The model was validated against existing experimental data.     

神经网络提高肝细胞癌磁共振波谱诊断正确率

通过评价31磷磁共振波谱(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)来辨别三种诊断类型:肝细胞癌,正常肝和肝硬化。运用反向传输神经网络(BP)和径向基函数神经网络(RBF)分析31P-MRS数据,分别建立神经网络模型,进行肝细胞癌的诊断分类以期提高识别率。实验结果证明,应用神经网络模型后,31P-MR波谱对活体肝细胞癌的诊断正确率从89.47%提高到97.3%,且BP更优于RBF。     

Manipulating the strength of organism–environment feedback increases nonlinearity and apparent hysteresis of ecosystem response to environmental change

Theory predicts that organism–environment feedbacks play a central role in how ecological communities respond to environmental change. Strong feedback causes greater nonlinearity between environmental change and ecosystem state, increases the likelihood of hysteresis in response to environmental change, and augments the possibility of alternative stable regimes. To illustrate these predictions and their dependence on a temporal scale, we simulated a minimal ecosystem model. To test the predictions, we manipulated the feedback strength between the metabolism and the dissolved oxygen concentration in an aquatic heterotrophic tri‐trophic community in microecosystems. The manipulation consisted of five levels, ranging from low to high feedback strength by altering the oxygen diffusivity: free gas exchange between the microcosm atmosphere and the external air (metabolism not strongly affecting environmental oxygen), with the regular addition of 200, 100, or 50 ml of air and no gas exchange. To test for nonlinearity and hysteresis in response to environmental change, all microecosystems experienced a gradual temperature increase from 15 to 25°C and then back to 15°C. We regularly measured the dissolved oxygen concentration, total biomass, and species abundance. Nonlinearity and hysteresis were higher in treatments with stronger organism–environment feedbacks. There was no evidence that stronger feedback increased the number of observed ecosystem states. These empirical results are in broad agreement with the theory that stronger feedback increases nonlinearity and hysteresis. They therefore represent one of the first direct empirical tests of the importance of feedback strength. However, we discuss several limitations of the study, which weaken confidence in this interpretation. Research demonstrating the causal effects of feedback strength on ecosystem responses to environmental change should be placed at the core of efforts to plan for sustainable ecosystems.