Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Energy performance forecasting of residential buildings using fuzzy approaches

The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.Peer ReviewedPostprint (published version

Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Application of dynamic vibration absorbers on double-deck circular railway tunnels to mitigate railway-induced ground-borne vibration

This dissertation is concerned with investigating the efficiency of dynamic vibration absorbers (DVAs) as measures to mitigate ground-borne vibrations induced by railway traffic in double-deck tunnels. The main topics of the dissertation are the coupling of a set of longitudinal distributions of DVAs to the interior floor of a double-deck tunnel dynamic model, the computation of the response of this coupled system due to train traffic and obtaining the optimum design parameters of the DVAs to minimize this response. To address the first concern, a methodology for coupling a set of longitudinal distributions of DVAs to any railway subsystem in the context of a theoretical dynamic model of railway infrastructure is developed. The optimum design parameters of the DVAs are obtained using an optimization process based on a genetic algorithm. The effectiveness of the DVAs is assessed by two response parameters, which are used as objective functions to be minimized in the optimization process: the energy flow radiated upwards by the tunnel and the maximum transient vibration value (MTVV) in the building near the tunnel. The model used to compute the former is a two-and-a-half dimensional (2.5D) semi-analytical model of a train-track-tunnel-soil system that considers a full-space soil model, and the one used to compute the latter is a hybrid experimental-numerical model of a train-track-tunnel-soil-building system. In the hybrid model, a numerical model of the track-tunnel system based on 2.5D coupled finite element-boundary element formulation along with a dynamic rigid multi-body model of the vehicle is used to compute the response in the tunnel wall, and then, the response in the building is computed using experimentally obtained transfer functions between the tunnel wall and the building. The triaxial response in the building is used to compute the MTVV. An alternative option to evaluate the MTVV in a building is to use a fully theoretical model of the train-track-tunnel-soil-building system. In the context of this modeling strategy, a computationally efficient method to calculate the 2.5D Green's functions of a layered soil is also presented. The results show that the DVAs would be an effective mitigation measure for railway-induced vibrations in double-deck tunnels as reductions up to 6.6 dB in total radiated energy flow and up to 3.3 dB in the vibration inside a nearby building are achieved in the simulations presented in this work.En esta tesis se estudia la eficiencia de los absorbedores de vibraciones dinámicos (DVAs) como medidas de mitigación de las vibraciones inducidas por infraestructuras ferroviarias aplicados a túneles ferroviarios de dos niveles. Los principales desarrollos de la tesis son el acoplamiento de un conjunto de distribuciones longitudinales de DVAs a la losa intermedia de un modelo dinámico de túnel de dos niveles, el cálculo de la respuesta de este sistema acoplado debido al paso del tren y la obtención de los parámetros óptimos de los DVAs para minimizar esta respuesta. Para abordar la primer punto, se ha desarrollado una metodología con el fin de acoplar un conjunto de distribuciones longitudinales de DVAs a cualquier subsistema ferroviario en el contexto de modelos teóricos de la dinámica de infraestructura ferroviarias. Los parámetros óptimos de los DVAs han sido obtenidos mediante un proceso de optimización basado en un algoritmo genético. La eficiencia de los DVAs se evalúa mediante dos quantificadores de la respuesta dinámica del sistema, los cuales se utilizan como funciones objetivo a minimizar en el proceso de optimización: el flujo de energía total radiado hacia arriba desde el túnel y el valor máximo de vibración transitoria (MTVV) en el forjada de un edificio cercano al túnel. El modelo utilizado para calcular el primero es un modelo semi-analítico del sistema vehículo-vía-túnel-terreno que considera un modelo de terreno de espacio completo, y el que se utiliza para calcular el segundo es un modelo híbrido experimental-numérico del sistema vehículo-vía-túnel-terreno-edificio. En el modelo híbrido, se utiliza un modelo numérico del sistema vía-túnel basado en la formulación acoplada de elementos finitos-elementos de contorno acoplados, formulada en el dominio del número de onda y la frecuencia, junto con un modelo dinámico multicuerpo del vehículo con el objetivo de calcular la respuesta en la pared del túnel. Luego, la respuesta en el edificio se calcula utilizando funciones de transferencia obtenidas experimentalmente entre la pared del túnel y el edificio. Para calcular el MTVV, se utiliza la respuesta triaxial en el edificio. Una opción alternativa para evaluar el MTVV en un edificio es utilizar un modelo totalmente teórico del sistema vehículo-vía-túnel-terreno-edificio. En el contexto de esta estrategia de modelado, también se presenta un método computacionalmente eficiente para calcular las funciones de Green de un terreno en capas en el dominio 2.5D. Los resultados muestran que los DVAs pueden ser una medida de mitigación efectiva para las vibraciones inducidas por infraestructuras ferroviarias en el marco de un túnel ferroviario de dos niveles, ya que en las simulaciones presentadas en esta tesis se alcanzan reducciones de hasta 6.6 dB en el flujo de energía total radiado y hasta 3.3 dB en la vibración dentro de un edificio cercano.Postprint (published version

Frequency-Aware Model Predictive Control

Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on the real system. Model errors can be a result of model simplifications, but also naturally arise when deploying the robot in unstructured and nondeterministic environments. Predominantly, compliant contacts and actuator dynamics lead to bandwidth limitations. While classical control methods provide tools to synthesize controllers that are robust to a class of model errors, such a notion is missing in modern trajectory optimization, which is solved in the time domain. We propose frequency-shaped cost functions to achieve robust solutions in the context of optimal control for legged robots. Through simulation and hardware experiments we show that motion plans can be made compatible with bandwidth limits set by actuators and contact dynamics. The smoothness of the model predictive solutions can be continuously tuned without compromising the feasibility of the problem. Experiments with the quadrupedal robot ANYmal, which is driven by highly-compliant series elastic actuators, showed significantly improved tracking performance of the planned motion, torque, and force trajectories and enabled the machine to walk robustly on terrain with unmodeled compliance

A stable and accurate control-volume technique based on integrated radial basis function networks for fluid-flow problems

Radial basis function networks (RBFNs) have been widely used in solving partial differential equations as they are able to provide fast convergence. Integrated RBFNs have the ability to avoid the problem of reduced convergence-rate caused by differentiation. This paper is concerned with the use of integrated RBFNs in the context of control-volume discretisations for the simulation of fluid-flow problems. Special attention is given to (i) the development of a stable high-order upwind scheme for the convection term and (ii) the development of a local high-order approximation scheme for the diffusion term. Benchmark problems including the lid-driven triangular-cavity flow are employed to validate the present technique. Accurate results at high values of the Reynolds number are obtained using relatively-coarse grids

Adaptive Tracking Controller for Real-Time Hybrid Simulation

Real-time hybrid simulation (RTHS) is a versatile and cost-effective testing method for studying the performance of structures subjected to dynamic loading. RTHS decomposes a structure into partitioned physical and numerical sub-structures that are coupled together through actuation systems. The sub-structuring approach is particularly attractive for studying large-scale problems since it allows for setting up large-scale structures with thousands of degrees of freedom in numerical simulations while specific components can be studied experimentally.The actuator dynamics generate an inevitable time delay in the overall system that affects the accuracy and stability of the simulation. Therefore, developing robust tracking control methodologies are necessary to mitigate these adverse effects. This research presents a state of the art review of tracking controllers for RTHS, and proposes a Conditional Adaptive Time Series (CATS) compensator based on the principles of the Adaptive Time Series compensator (ATS). The accuracy of the proposed controller is evaluated with a benchmark problem of a three-story building with a single degree of freedom (SDOF) in a realistic virtual RTHS (vRTHS). In addition, the accuracy of the proposed method is evaluated for seven numerical integration algorithms suitable for RTHS

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS